Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.7 KiB
Average record size in memory150.3 B

Variable types

Text2
Categorical2
Numeric13

Alerts

jan_visit_stats_co is highly overall correlated with feb_visit_stats_co and 11 other fieldsHigh correlation
feb_visit_stats_co is highly overall correlated with jan_visit_stats_co and 8 other fieldsHigh correlation
mar_visit_stats_co is highly overall correlated with apr_visit_stats_co and 8 other fieldsHigh correlation
apr_visit_stats_co is highly overall correlated with jan_visit_stats_co and 9 other fieldsHigh correlation
may_visit_stats_co is highly overall correlated with jan_visit_stats_co and 9 other fieldsHigh correlation
jun_visit_stats_co is highly overall correlated with jan_visit_stats_co and 11 other fieldsHigh correlation
july_visit_stats_co is highly overall correlated with jan_visit_stats_co and 11 other fieldsHigh correlation
aug_visit_stats_co is highly overall correlated with jan_visit_stats_co and 11 other fieldsHigh correlation
sep_visit_stats_co is highly overall correlated with jan_visit_stats_co and 10 other fieldsHigh correlation
oct_visit_stats_co is highly overall correlated with jan_visit_stats_co and 11 other fieldsHigh correlation
nov_visit_stats_co is highly overall correlated with jan_visit_stats_co and 11 other fieldsHigh correlation
dec_visit_stats_co is highly overall correlated with jan_visit_stats_co and 7 other fieldsHigh correlation
accmlt_visit_stats_co is highly overall correlated with jan_visit_stats_co and 11 other fieldsHigh correlation
trrsrt_addr is highly overall correlated with jan_visit_stats_co and 1 other fieldsHigh correlation
brand_nm is highly overall correlated with trrsrt_addrHigh correlation
trrsrt_cd has unique valuesUnique
trrsrt_nm has unique valuesUnique
jan_visit_stats_co has 34 (34.0%) zerosZeros
feb_visit_stats_co has 54 (54.0%) zerosZeros
mar_visit_stats_co has 75 (75.0%) zerosZeros
apr_visit_stats_co has 53 (53.0%) zerosZeros
may_visit_stats_co has 41 (41.0%) zerosZeros
jun_visit_stats_co has 38 (38.0%) zerosZeros
july_visit_stats_co has 35 (35.0%) zerosZeros
aug_visit_stats_co has 40 (40.0%) zerosZeros
sep_visit_stats_co has 67 (67.0%) zerosZeros
oct_visit_stats_co has 33 (33.0%) zerosZeros
nov_visit_stats_co has 44 (44.0%) zerosZeros
dec_visit_stats_co has 81 (81.0%) zerosZeros
accmlt_visit_stats_co has 18 (18.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:40:07.628578
Analysis finished2023-12-10 09:40:40.641704
Duration33.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

trrsrt_cd
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:40:40.901130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1200
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowP00000000907
2nd rowP00000002560
3rd rowP00000000909
4th rowP00000000910
5th rowP00000000911
ValueCountFrequency (%)
p00000000907 1
 
1.0%
p00000001044 1
 
1.0%
p00000001055 1
 
1.0%
p00000001054 1
 
1.0%
p00000001053 1
 
1.0%
p00000001052 1
 
1.0%
p00000001051 1
 
1.0%
p00000001050 1
 
1.0%
p00000001049 1
 
1.0%
p00000001048 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:40:41.467997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 822
68.5%
P 100
 
8.3%
1 91
 
7.6%
9 51
 
4.2%
5 24
 
2.0%
4 24
 
2.0%
6 21
 
1.8%
7 18
 
1.5%
2 17
 
1.4%
3 17
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1100
91.7%
Uppercase Letter 100
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 822
74.7%
1 91
 
8.3%
9 51
 
4.6%
5 24
 
2.2%
4 24
 
2.2%
6 21
 
1.9%
7 18
 
1.6%
2 17
 
1.5%
3 17
 
1.5%
8 15
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
P 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1100
91.7%
Latin 100
 
8.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 822
74.7%
1 91
 
8.3%
9 51
 
4.6%
5 24
 
2.2%
4 24
 
2.2%
6 21
 
1.9%
7 18
 
1.6%
2 17
 
1.5%
3 17
 
1.5%
8 15
 
1.4%
Latin
ValueCountFrequency (%)
P 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 822
68.5%
P 100
 
8.3%
1 91
 
7.6%
9 51
 
4.2%
5 24
 
2.0%
4 24
 
2.0%
6 21
 
1.8%
7 18
 
1.5%
2 17
 
1.4%
3 17
 
1.4%

trrsrt_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:40:41.945578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length13.53
Min length7

Characters and Unicode

Total characters1353
Distinct characters180
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row[안동]하회마을_성인
2nd row[청도]홍차리에_애프터눈티
3rd row[안동]안동시립민속박물관_성인
4th row[안동]안동전통문화 컨텐츠박물관_성인
5th row[안동]도산서원_성인
ValueCountFrequency (%)
울진]엑스포공원 6
 
5.0%
울진]금강송 4
 
3.3%
예천 2
 
1.7%
안동]안동전통문화 2
 
1.7%
문경]문경새재 2
 
1.7%
문경]불정자연휴양림 2
 
1.7%
문경]오미자 2
 
1.7%
안동]하회마을_성인 1
 
0.8%
경주]포석정_성인 1
 
0.8%
곤충여행체험관_청소년 1
 
0.8%
Other values (98) 98
81.0%
2023-12-10T18:40:42.637305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 100
 
7.4%
] 100
 
7.4%
_ 92
 
6.8%
74
 
5.5%
55
 
4.1%
48
 
3.5%
46
 
3.4%
28
 
2.1%
27
 
2.0%
21
 
1.6%
Other values (170) 762
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1009
74.6%
Open Punctuation 106
 
7.8%
Close Punctuation 106
 
7.8%
Connector Punctuation 92
 
6.8%
Space Separator 21
 
1.6%
Other Punctuation 11
 
0.8%
Decimal Number 8
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
74
 
7.3%
55
 
5.5%
48
 
4.8%
46
 
4.6%
28
 
2.8%
27
 
2.7%
21
 
2.1%
21
 
2.1%
20
 
2.0%
17
 
1.7%
Other values (158) 652
64.6%
Decimal Number
ValueCountFrequency (%)
2 2
25.0%
0 2
25.0%
9 2
25.0%
1 2
25.0%
Open Punctuation
ValueCountFrequency (%)
[ 100
94.3%
( 6
 
5.7%
Close Punctuation
ValueCountFrequency (%)
] 100
94.3%
) 6
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/ 9
81.8%
& 2
 
18.2%
Connector Punctuation
ValueCountFrequency (%)
_ 92
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1009
74.6%
Common 344
 
25.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
74
 
7.3%
55
 
5.5%
48
 
4.8%
46
 
4.6%
28
 
2.8%
27
 
2.7%
21
 
2.1%
21
 
2.1%
20
 
2.0%
17
 
1.7%
Other values (158) 652
64.6%
Common
ValueCountFrequency (%)
[ 100
29.1%
] 100
29.1%
_ 92
26.7%
21
 
6.1%
/ 9
 
2.6%
) 6
 
1.7%
( 6
 
1.7%
& 2
 
0.6%
2 2
 
0.6%
0 2
 
0.6%
Other values (2) 4
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1009
74.6%
ASCII 344
 
25.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
[ 100
29.1%
] 100
29.1%
_ 92
26.7%
21
 
6.1%
/ 9
 
2.6%
) 6
 
1.7%
( 6
 
1.7%
& 2
 
0.6%
2 2
 
0.6%
0 2
 
0.6%
Other values (2) 4
 
1.2%
Hangul
ValueCountFrequency (%)
74
 
7.3%
55
 
5.5%
48
 
4.8%
46
 
4.6%
28
 
2.8%
27
 
2.7%
21
 
2.1%
21
 
2.1%
20
 
2.0%
17
 
1.7%
Other values (158) 652
64.6%

trrsrt_addr
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경북 포항시 북구 두호동 1017
 
6
경북 울진군 근남면 수산리 63
 
6
경북 성주군 수륜면 수성리 104 어울림마당
 
6
경북 울진군 금강송면 십이령로 552
 
4
경북 경주시 배동 454-3
 
3
Other values (37)
75 

Length

Max length30
Median length24
Mean length18.48
Min length13

Unique

Unique7 ?
Unique (%)7.0%

Sample

1st row경북 안동시 풍천면 전서로 186
2nd row경상북도 청도군 화양읍 합천리 590
3rd row경북 안동시 민속촌길 13
4th row경북 안동시 서동문로 203
5th row경북 안동시 도산면 도산서원길 154

Common Values

ValueCountFrequency (%)
경북 포항시 북구 두호동 1017 6
 
6.0%
경북 울진군 근남면 수산리 63 6
 
6.0%
경북 성주군 수륜면 수성리 104 어울림마당 6
 
6.0%
경북 울진군 금강송면 십이령로 552 4
 
4.0%
경북 경주시 배동 454-3 3
 
3.0%
경북 경주시 인왕동 517 3
 
3.0%
경북 경주시 황남동 33 일대 3
 
3.0%
경북 경주시 탑동 67-1 3
 
3.0%
경북 경주시 서악동 842 3
 
3.0%
경상북도 청도군 화양읍 합천리 590 3
 
3.0%
Other values (32) 60
60.0%

Length

2023-12-10T18:40:42.912530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경북 85
 
17.5%
경주시 18
 
3.7%
안동시 17
 
3.5%
울진군 17
 
3.5%
경상북도 14
 
2.9%
포항시 11
 
2.3%
근남면 10
 
2.1%
상주시 10
 
2.1%
북구 9
 
1.9%
문경시 8
 
1.6%
Other values (111) 286
59.0%

brand_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경주 e누리
18 
안동 e누리
17 
울진 e누리
17 
포항 e누리
11 
상주 e누리
10 
Other values (5)
27 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row안동 e누리
2nd row청도 e누리
3rd row안동 e누리
4th row안동 e누리
5th row안동 e누리

Common Values

ValueCountFrequency (%)
경주 e누리 18
18.0%
안동 e누리 17
17.0%
울진 e누리 17
17.0%
포항 e누리 11
11.0%
상주 e누리 10
10.0%
문경 e누리 8
8.0%
영주 e누리 6
 
6.0%
성주 e누리 6
 
6.0%
예천 e누리 4
 
4.0%
청도 e누리 3
 
3.0%

Length

2023-12-10T18:40:43.145646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:43.363515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e누리 100
50.0%
경주 18
 
9.0%
안동 17
 
8.5%
울진 17
 
8.5%
포항 11
 
5.5%
상주 10
 
5.0%
문경 8
 
4.0%
영주 6
 
3.0%
성주 6
 
3.0%
예천 4
 
2.0%

jan_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.45
Minimum0
Maximum896
Zeros34
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:43.913039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q326
95-th percentile220.2
Maximum896
Range896
Interquartile range (IQR)26

Descriptive statistics

Standard deviation128.0136
Coefficient of variation (CV)2.6978631
Kurtosis24.00758
Mean47.45
Median Absolute Deviation (MAD)7
Skewness4.5839725
Sum4745
Variance16387.482
MonotonicityNot monotonic
2023-12-10T18:40:44.096999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 34
34.0%
12 8
 
8.0%
4 8
 
8.0%
18 5
 
5.0%
2 5
 
5.0%
26 4
 
4.0%
14 2
 
2.0%
94 2
 
2.0%
22 2
 
2.0%
6 2
 
2.0%
Other values (24) 28
28.0%
ValueCountFrequency (%)
0 34
34.0%
1 1
 
1.0%
2 5
 
5.0%
4 8
 
8.0%
6 2
 
2.0%
8 2
 
2.0%
10 2
 
2.0%
12 8
 
8.0%
14 2
 
2.0%
18 5
 
5.0%
ValueCountFrequency (%)
896 1
1.0%
618 1
1.0%
500 1
1.0%
321 1
1.0%
300 1
1.0%
216 1
1.0%
198 1
1.0%
169 1
1.0%
168 1
1.0%
136 1
1.0%

feb_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.85
Minimum0
Maximum841
Zeros54
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:44.272921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile94.55
Maximum841
Range841
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation109.32099
Coefficient of variation (CV)3.5436301
Kurtosis34.969946
Mean30.85
Median Absolute Deviation (MAD)0
Skewness5.6005096
Sum3085
Variance11951.078
MonotonicityNot monotonic
2023-12-10T18:40:44.459034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 54
54.0%
4 8
 
8.0%
2 5
 
5.0%
24 3
 
3.0%
8 3
 
3.0%
14 2
 
2.0%
18 2
 
2.0%
12 2
 
2.0%
22 2
 
2.0%
5 2
 
2.0%
Other values (16) 17
 
17.0%
ValueCountFrequency (%)
0 54
54.0%
2 5
 
5.0%
4 8
 
8.0%
5 2
 
2.0%
6 1
 
1.0%
8 3
 
3.0%
12 2
 
2.0%
14 2
 
2.0%
16 1
 
1.0%
18 2
 
2.0%
ValueCountFrequency (%)
841 1
1.0%
475 1
1.0%
462 1
1.0%
234 1
1.0%
181 1
1.0%
90 1
1.0%
84 1
1.0%
76 2
2.0%
68 1
1.0%
58 1
1.0%

mar_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.16
Minimum0
Maximum881
Zeros75
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:44.644440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile138.35
Maximum881
Range881
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation109.93384
Coefficient of variation (CV)4.5502417
Kurtosis41.192667
Mean24.16
Median Absolute Deviation (MAD)0
Skewness6.0719887
Sum2416
Variance12085.449
MonotonicityNot monotonic
2023-12-10T18:40:44.845980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 75
75.0%
2 7
 
7.0%
5 3
 
3.0%
4 3
 
3.0%
3 2
 
2.0%
881 1
 
1.0%
488 1
 
1.0%
378 1
 
1.0%
197 1
 
1.0%
183 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
0 75
75.0%
1 1
 
1.0%
2 7
 
7.0%
3 2
 
2.0%
4 3
 
3.0%
5 3
 
3.0%
6 1
 
1.0%
20 1
 
1.0%
79 1
 
1.0%
136 1
 
1.0%
ValueCountFrequency (%)
881 1
 
1.0%
488 1
 
1.0%
378 1
 
1.0%
197 1
 
1.0%
183 1
 
1.0%
136 1
 
1.0%
79 1
 
1.0%
20 1
 
1.0%
6 1
 
1.0%
5 3
3.0%

apr_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.98
Minimum0
Maximum1201
Zeros53
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:45.043763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36.25
95-th percentile294.75
Maximum1201
Range1201
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation164.23763
Coefficient of variation (CV)3.651348
Kurtosis28.60711
Mean44.98
Median Absolute Deviation (MAD)0
Skewness5.0327963
Sum4498
Variance26974
MonotonicityNot monotonic
2023-12-10T18:40:45.247722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 53
53.0%
4 6
 
6.0%
6 6
 
6.0%
2 5
 
5.0%
5 3
 
3.0%
13 2
 
2.0%
7 2
 
2.0%
8 2
 
2.0%
12 1
 
1.0%
10 1
 
1.0%
Other values (19) 19
 
19.0%
ValueCountFrequency (%)
0 53
53.0%
1 1
 
1.0%
2 5
 
5.0%
3 1
 
1.0%
4 6
 
6.0%
5 3
 
3.0%
6 6
 
6.0%
7 2
 
2.0%
8 2
 
2.0%
10 1
 
1.0%
ValueCountFrequency (%)
1201 1
1.0%
728 1
1.0%
607 1
1.0%
465 1
1.0%
347 1
1.0%
292 1
1.0%
254 1
1.0%
190 1
1.0%
58 1
1.0%
37 1
1.0%

may_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.11
Minimum0
Maximum3824
Zeros41
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:45.442981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q323.25
95-th percentile845.4
Maximum3824
Range3824
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation513.83669
Coefficient of variation (CV)3.469291
Kurtosis30.578847
Mean148.11
Median Absolute Deviation (MAD)4
Skewness5.1757465
Sum14811
Variance264028.14
MonotonicityNot monotonic
2023-12-10T18:40:45.718387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 41
41.0%
4 5
 
5.0%
8 3
 
3.0%
1 3
 
3.0%
9 3
 
3.0%
11 3
 
3.0%
5 2
 
2.0%
18 2
 
2.0%
2 2
 
2.0%
20 2
 
2.0%
Other values (31) 34
34.0%
ValueCountFrequency (%)
0 41
41.0%
1 3
 
3.0%
2 2
 
2.0%
3 1
 
1.0%
4 5
 
5.0%
5 2
 
2.0%
8 3
 
3.0%
9 3
 
3.0%
11 3
 
3.0%
13 2
 
2.0%
ValueCountFrequency (%)
3824 1
1.0%
2424 1
1.0%
1768 1
1.0%
1175 1
1.0%
1119 1
1.0%
831 1
1.0%
731 1
1.0%
602 1
1.0%
475 1
1.0%
353 1
1.0%

jun_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.79
Minimum0
Maximum2323
Zeros38
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:45.962653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q341.5
95-th percentile1060.9
Maximum2323
Range2323
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation409.02667
Coefficient of variation (CV)2.7676207
Kurtosis13.310657
Mean147.79
Median Absolute Deviation (MAD)4
Skewness3.5946076
Sum14779
Variance167302.81
MonotonicityNot monotonic
2023-12-10T18:40:46.186785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 38
38.0%
4 8
 
8.0%
6 5
 
5.0%
2 5
 
5.0%
10 3
 
3.0%
9 3
 
3.0%
1 3
 
3.0%
72 2
 
2.0%
16 2
 
2.0%
18 2
 
2.0%
Other values (29) 29
29.0%
ValueCountFrequency (%)
0 38
38.0%
1 3
 
3.0%
2 5
 
5.0%
4 8
 
8.0%
6 5
 
5.0%
9 3
 
3.0%
10 3
 
3.0%
12 1
 
1.0%
13 1
 
1.0%
16 2
 
2.0%
ValueCountFrequency (%)
2323 1
1.0%
1924 1
1.0%
1700 1
1.0%
1387 1
1.0%
1192 1
1.0%
1054 1
1.0%
781 1
1.0%
754 1
1.0%
660 1
1.0%
480 1
1.0%

july_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.94
Minimum0
Maximum3514
Zeros35
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:46.426558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q376.5
95-th percentile1038.4
Maximum3514
Range3514
Interquartile range (IQR)76.5

Descriptive statistics

Standard deviation511.69249
Coefficient of variation (CV)2.7226375
Kurtosis20.736872
Mean187.94
Median Absolute Deviation (MAD)8
Skewness4.2160048
Sum18794
Variance261829.21
MonotonicityNot monotonic
2023-12-10T18:40:46.711503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 35
35.0%
6 6
 
6.0%
8 4
 
4.0%
2 4
 
4.0%
14 3
 
3.0%
4 3
 
3.0%
10 2
 
2.0%
18 2
 
2.0%
38 2
 
2.0%
12 2
 
2.0%
Other values (36) 37
37.0%
ValueCountFrequency (%)
0 35
35.0%
2 4
 
4.0%
4 3
 
3.0%
6 6
 
6.0%
8 4
 
4.0%
10 2
 
2.0%
12 2
 
2.0%
14 3
 
3.0%
18 2
 
2.0%
22 1
 
1.0%
ValueCountFrequency (%)
3514 1
1.0%
2036 1
1.0%
1890 1
1.0%
1832 1
1.0%
1236 1
1.0%
1028 1
1.0%
994 1
1.0%
854 1
1.0%
626 1
1.0%
600 1
1.0%

aug_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.74
Minimum0
Maximum3924
Zeros40
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:46.997064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q369.5
95-th percentile546.7
Maximum3924
Range3924
Interquartile range (IQR)69.5

Descriptive statistics

Standard deviation537.60281
Coefficient of variation (CV)3.4519251
Kurtosis30.057857
Mean155.74
Median Absolute Deviation (MAD)6
Skewness5.2465007
Sum15574
Variance289016.78
MonotonicityNot monotonic
2023-12-10T18:40:47.342843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 40
40.0%
10 6
 
6.0%
6 4
 
4.0%
4 4
 
4.0%
38 3
 
3.0%
2 3
 
3.0%
14 3
 
3.0%
40 2
 
2.0%
60 2
 
2.0%
24 2
 
2.0%
Other values (30) 31
31.0%
ValueCountFrequency (%)
0 40
40.0%
2 3
 
3.0%
4 4
 
4.0%
6 4
 
4.0%
8 1
 
1.0%
10 6
 
6.0%
14 3
 
3.0%
18 2
 
2.0%
24 2
 
2.0%
34 1
 
1.0%
ValueCountFrequency (%)
3924 1
1.0%
2648 1
1.0%
2108 1
1.0%
1346 1
1.0%
1206 1
1.0%
512 1
1.0%
368 1
1.0%
310 1
1.0%
226 1
1.0%
218 1
1.0%

sep_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.7
Minimum0
Maximum1100
Zeros67
Zeros (%)67.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:47.639626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile181.5
Maximum1100
Range1100
Interquartile range (IQR)6

Descriptive statistics

Standard deviation147.35688
Coefficient of variation (CV)3.9086706
Kurtosis31.961059
Mean37.7
Median Absolute Deviation (MAD)0
Skewness5.3859747
Sum3770
Variance21714.051
MonotonicityNot monotonic
2023-12-10T18:40:47.961026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 67
67.0%
6 4
 
4.0%
2 3
 
3.0%
10 3
 
3.0%
16 2
 
2.0%
4 2
 
2.0%
12 2
 
2.0%
76 1
 
1.0%
20 1
 
1.0%
26 1
 
1.0%
Other values (14) 14
 
14.0%
ValueCountFrequency (%)
0 67
67.0%
2 3
 
3.0%
4 2
 
2.0%
6 4
 
4.0%
10 3
 
3.0%
12 2
 
2.0%
16 2
 
2.0%
20 1
 
1.0%
22 1
 
1.0%
24 1
 
1.0%
ValueCountFrequency (%)
1100 1
1.0%
658 1
1.0%
600 1
1.0%
364 1
1.0%
324 1
1.0%
174 1
1.0%
76 1
1.0%
64 1
1.0%
48 1
1.0%
44 1
1.0%

oct_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.58
Minimum0
Maximum5638
Zeros33
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:48.353624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q386.5
95-th percentile799.2
Maximum5638
Range5638
Interquartile range (IQR)86.5

Descriptive statistics

Standard deviation704.51257
Coefficient of variation (CV)3.5657079
Kurtosis39.90266
Mean197.58
Median Absolute Deviation (MAD)12
Skewness5.94764
Sum19758
Variance496337.96
MonotonicityNot monotonic
2023-12-10T18:40:48.568071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 33
33.0%
14 5
 
5.0%
8 4
 
4.0%
6 4
 
4.0%
2 4
 
4.0%
4 3
 
3.0%
12 3
 
3.0%
162 2
 
2.0%
16 2
 
2.0%
84 2
 
2.0%
Other values (38) 38
38.0%
ValueCountFrequency (%)
0 33
33.0%
2 4
 
4.0%
4 3
 
3.0%
6 4
 
4.0%
8 4
 
4.0%
12 3
 
3.0%
14 5
 
5.0%
16 2
 
2.0%
20 1
 
1.0%
22 1
 
1.0%
ValueCountFrequency (%)
5638 1
1.0%
3268 1
1.0%
2234 1
1.0%
1574 1
1.0%
1202 1
1.0%
778 1
1.0%
450 1
1.0%
356 1
1.0%
332 1
1.0%
312 1
1.0%

nov_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.58
Minimum0
Maximum668
Zeros44
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:48.777529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q324
95-th percentile156.4
Maximum668
Range668
Interquartile range (IQR)24

Descriptive statistics

Standard deviation92.641721
Coefficient of variation (CV)2.679055
Kurtosis25.584338
Mean34.58
Median Absolute Deviation (MAD)4
Skewness4.6828545
Sum3458
Variance8582.4885
MonotonicityNot monotonic
2023-12-10T18:40:49.023796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 44
44.0%
8 9
 
9.0%
10 6
 
6.0%
4 5
 
5.0%
52 3
 
3.0%
2 2
 
2.0%
44 2
 
2.0%
26 2
 
2.0%
24 2
 
2.0%
14 2
 
2.0%
Other values (23) 23
23.0%
ValueCountFrequency (%)
0 44
44.0%
2 2
 
2.0%
4 5
 
5.0%
6 1
 
1.0%
8 9
 
9.0%
10 6
 
6.0%
12 1
 
1.0%
14 2
 
2.0%
16 1
 
1.0%
18 1
 
1.0%
ValueCountFrequency (%)
668 1
1.0%
408 1
1.0%
374 1
1.0%
238 1
1.0%
202 1
1.0%
154 1
1.0%
152 1
1.0%
134 1
1.0%
116 1
1.0%
98 1
1.0%

dec_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.32
Minimum0
Maximum128
Zeros81
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:49.239637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile27.1
Maximum128
Range128
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.490935
Coefficient of variation (CV)3.6637097
Kurtosis23.286712
Mean5.32
Median Absolute Deviation (MAD)0
Skewness4.7196576
Sum532
Variance379.89657
MonotonicityNot monotonic
2023-12-10T18:40:49.434087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 81
81.0%
4 5
 
5.0%
10 3
 
3.0%
14 2
 
2.0%
2 2
 
2.0%
128 1
 
1.0%
90 1
 
1.0%
96 1
 
1.0%
54 1
 
1.0%
48 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
0 81
81.0%
2 2
 
2.0%
4 5
 
5.0%
8 1
 
1.0%
10 3
 
3.0%
14 2
 
2.0%
26 1
 
1.0%
48 1
 
1.0%
54 1
 
1.0%
90 1
 
1.0%
ValueCountFrequency (%)
128 1
 
1.0%
96 1
 
1.0%
90 1
 
1.0%
54 1
 
1.0%
48 1
 
1.0%
26 1
 
1.0%
14 2
 
2.0%
10 3
3.0%
8 1
 
1.0%
4 5
5.0%

accmlt_visit_stats_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1062.2
Minimum0
Maximum24539
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:49.656276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5
median66
Q3539
95-th percentile5905.2
Maximum24539
Range24539
Interquartile range (IQR)533.5

Descriptive statistics

Standard deviation3256.6821
Coefficient of variation (CV)3.0659782
Kurtosis30.927019
Mean1062.2
Median Absolute Deviation (MAD)66
Skewness5.1788229
Sum106220
Variance10605978
MonotonicityNot monotonic
2023-12-10T18:40:49.901983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
18.0%
4 5
 
5.0%
6 3
 
3.0%
48 2
 
2.0%
75 2
 
2.0%
37 2
 
2.0%
43 2
 
2.0%
1155 1
 
1.0%
98 1
 
1.0%
61 1
 
1.0%
Other values (63) 63
63.0%
ValueCountFrequency (%)
0 18
18.0%
1 1
 
1.0%
3 1
 
1.0%
4 5
 
5.0%
6 3
 
3.0%
18 1
 
1.0%
22 1
 
1.0%
23 1
 
1.0%
25 1
 
1.0%
27 1
 
1.0%
ValueCountFrequency (%)
24539 1
1.0%
14347 1
1.0%
12757 1
1.0%
7034 1
1.0%
6593 1
1.0%
5869 1
1.0%
4013 1
1.0%
3232 1
1.0%
3050 1
1.0%
2972 1
1.0%

Interactions

2023-12-10T18:40:38.014100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:09.000980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.472757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:13.709450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:15.890580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.900569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:20.113469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:22.702635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:25.955424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:28.310372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:30.504442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:32.987938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:35.455443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:38.180422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:09.260219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.602946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:13.888543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.037632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.043601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:20.769715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:22.970681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:26.233993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:28.458465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:30.641342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:33.175517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:35.599504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:38.336416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:09.403059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.713084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:14.057847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.180387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.186672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:20.975252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:23.186892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:26.410447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:28.691847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:30.780635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:33.353843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:35.734462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:38.476182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:09.842132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.836066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:14.212357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.317327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.350303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:21.157414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:23.475097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:26.577312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:28.861411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:30.967384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:33.517833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:35.887010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:38.644224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:09.979128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.979144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:14.370512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.451748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.508030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:21.304183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:23.650705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:26.737844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:29.003577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:31.150956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:33.705023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:36.031475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:38.798367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:10.139626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:12.213135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:14.534145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.605957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.666695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:21.461053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:23.871780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:26.893784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:29.184426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:31.348954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:33.885640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:36.232147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:38.918624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:10.312828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:12.387791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:14.658309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.755562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.837908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:21.651149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:24.063184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:27.035255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:29.317573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:31.512165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:34.027893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:36.411139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:39.082387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:10.499525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:12.679103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:14.844109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.940148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.031761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:21.825479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:24.425866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:27.194967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:29.534236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:31.671422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:34.202615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:36.681381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:39.238071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:10.695957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:12.865504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:15.017557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.096814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.254020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:21.971443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:24.669499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:27.372247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:29.724764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:32.210548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:34.560357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:36.913231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:39.369474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:10.868887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:13.013057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:15.186307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.263936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.446422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:22.116129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:24.843942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:27.554584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:29.904204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:32.350468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:34.760481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:37.141450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:39.498705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.046162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:13.151268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:15.337980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.428532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.620893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:22.249612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:25.020227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:27.758444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:30.042023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:32.488828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:34.935732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:37.344604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:39.647310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.202199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:13.319782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:15.563023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.604302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.790169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:22.409691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:25.339562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:27.940070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:30.196378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:32.655509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:35.103336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:37.572720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:39.787719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.341901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:13.524333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:15.727016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.758351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.956039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:22.555933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:25.683818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:28.108397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:30.353577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:32.820774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:35.308643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:37.755304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:40:50.068386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
trrsrt_cdtrrsrt_nmtrrsrt_addrbrand_nmjan_visit_stats_cofeb_visit_stats_comar_visit_stats_coapr_visit_stats_comay_visit_stats_cojun_visit_stats_cojuly_visit_stats_coaug_visit_stats_cosep_visit_stats_cooct_visit_stats_conov_visit_stats_codec_visit_stats_coaccmlt_visit_stats_co
trrsrt_cd1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
trrsrt_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
trrsrt_addr1.0001.0001.0001.0000.9390.6690.6210.6300.7240.7600.6490.7280.7280.7280.7380.8190.640
brand_nm1.0001.0001.0001.0000.3140.5890.3790.3300.3860.3460.3730.4400.4400.4400.4830.4250.601
jan_visit_stats_co1.0001.0000.9390.3141.0000.8900.9030.9700.9700.8400.7600.9130.8630.9130.9780.9630.831
feb_visit_stats_co1.0001.0000.6690.5890.8901.0000.9090.8750.9060.9100.8410.9090.9090.9090.9320.9060.982
mar_visit_stats_co1.0001.0000.6210.3790.9030.9091.0000.9430.9600.9800.9580.9950.9750.9950.9440.8790.900
apr_visit_stats_co1.0001.0000.6300.3300.9700.8750.9431.0000.9860.9000.8250.9430.9250.9430.9800.9770.885
may_visit_stats_co1.0001.0000.7240.3860.9700.9060.9600.9861.0000.9610.9020.9600.9030.9600.9860.9690.965
jun_visit_stats_co1.0001.0000.7600.3460.8400.9100.9800.9000.9611.0000.9890.9720.9270.9720.8910.8560.951
july_visit_stats_co1.0001.0000.6490.3730.7600.8410.9580.8250.9020.9891.0000.9580.9580.9580.8220.8400.891
aug_visit_stats_co1.0001.0000.7280.4400.9130.9090.9950.9430.9600.9720.9581.0000.9951.0000.9600.9440.900
sep_visit_stats_co1.0001.0000.7280.4400.8630.9090.9750.9250.9030.9270.9580.9951.0000.9950.9031.0000.900
oct_visit_stats_co1.0001.0000.7280.4400.9130.9090.9950.9430.9600.9720.9581.0000.9951.0000.9600.9440.900
nov_visit_stats_co1.0001.0000.7380.4830.9780.9320.9440.9800.9860.8910.8220.9600.9030.9601.0000.9690.882
dec_visit_stats_co1.0001.0000.8190.4250.9630.9060.8790.9770.9690.8560.8400.9441.0000.9440.9691.0000.897
accmlt_visit_stats_co1.0001.0000.6400.6010.8310.9820.9000.8850.9650.9510.8910.9000.9000.9000.8820.8971.000
2023-12-10T18:40:50.398016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
trrsrt_addrbrand_nm
trrsrt_addr1.0000.803
brand_nm0.8031.000
2023-12-10T18:40:50.658631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
jan_visit_stats_cofeb_visit_stats_comar_visit_stats_coapr_visit_stats_comay_visit_stats_cojun_visit_stats_cojuly_visit_stats_coaug_visit_stats_cosep_visit_stats_cooct_visit_stats_conov_visit_stats_codec_visit_stats_coaccmlt_visit_stats_cotrrsrt_addrbrand_nm
jan_visit_stats_co1.0000.8300.3850.5400.5690.6140.6800.7140.5890.6860.6520.6370.7510.5400.159
feb_visit_stats_co0.8301.0000.4260.4450.4770.5580.6120.7910.6450.6670.6800.6690.7080.2940.275
mar_visit_stats_co0.3850.4261.0000.7330.6740.6510.5050.5140.5320.6600.6330.3260.6030.2300.203
apr_visit_stats_co0.5400.4450.7331.0000.8540.8340.6900.6430.6190.8030.7780.3870.7740.2220.168
may_visit_stats_co0.5690.4770.6740.8541.0000.8960.7190.7130.6000.8500.8290.4990.8700.2830.201
jun_visit_stats_co0.6140.5580.6510.8340.8961.0000.8330.8080.6130.8940.8650.5430.9180.3080.161
july_visit_stats_co0.6800.6120.5050.6900.7190.8331.0000.8620.6000.8490.7920.6410.8980.2460.200
aug_visit_stats_co0.7140.7910.5140.6430.7130.8080.8621.0000.6850.8700.8270.6730.9120.3010.243
sep_visit_stats_co0.5890.6450.5320.6190.6000.6130.6000.6851.0000.6880.6380.4620.6540.3010.243
oct_visit_stats_co0.6860.6670.6600.8030.8500.8940.8490.8700.6881.0000.9250.5490.9400.3010.243
nov_visit_stats_co0.6520.6800.6330.7780.8290.8650.7920.8270.6380.9251.0000.5850.8920.2940.263
dec_visit_stats_co0.6370.6690.3260.3870.4990.5430.6410.6730.4620.5490.5851.0000.6100.3670.226
accmlt_visit_stats_co0.7510.7080.6030.7740.8700.9180.8980.9120.6540.9400.8920.6101.0000.2740.282
trrsrt_addr0.5400.2940.2300.2220.2830.3080.2460.3010.3010.3010.2940.3670.2741.0000.803
brand_nm0.1590.2750.2030.1680.2010.1610.2000.2430.2430.2430.2630.2260.2820.8031.000

Missing values

2023-12-10T18:40:39.995866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:40:40.430116image/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

trrsrt_cdtrrsrt_nmtrrsrt_addrbrand_nmjan_visit_stats_cofeb_visit_stats_comar_visit_stats_coapr_visit_stats_comay_visit_stats_cojun_visit_stats_cojuly_visit_stats_coaug_visit_stats_cosep_visit_stats_cooct_visit_stats_conov_visit_stats_codec_visit_stats_coaccmlt_visit_stats_co
0P00000000907[안동]하회마을_성인경북 안동시 풍천면 전서로 186안동 e누리1365833748129172190442409801155
1P00000002560[청도]홍차리에_애프터눈티경상북도 청도군 화양읍 합천리 590청도 e누리0000000000000
2P00000000909[안동]안동시립민속박물관_성인경북 안동시 민속촌길 13안동 e누리26140000814080070
3P00000000910[안동]안동전통문화 컨텐츠박물관_성인경북 안동시 서동문로 203안동 e누리2680000010040048
4P00000000911[안동]도산서원_성인경북 안동시 도산면 도산서원길 154안동 e누리2643151722404010128300335
5P00000000912[안동]이육사문학관_성인경북 안동시 도산면 백운로 525안동 e누리1440000126080044
6P00000000913[안동]하회마을_소인경북 안동시 풍천면 전서로 186안동 e누리9442020155222401686320419
7P00000002561[청도]홍차리에_티릴레이경상북도 청도군 화양읍 합천리 590청도 e누리0000000000000
8P00000000915[안동]안동시립민속박물관_소인경북 안동시 민속촌길 13안동 e누리2212000026000042
9P00000000916[안동]안동전통문화 컨텐츠박물관_소인경북 안동시 서동문로 203안동 e누리264000004000034
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90P00000001076[포항]새천년기념관_소인경북 포항시 남구 호미곶면 해맞이로 136포항 e누리00022101438016220104
91P00000001077[포항]요트데이_주간/성인경북 포항시 북구 두호동 1017포항 e누리0000003621000048
92P00000001078[포항]요트데이_주간/청소년경북 포항시 북구 두호동 1017포항 e누리0000000000000
93P00000001079[포항]요트데이_주간/소인경북 포항시 북구 두호동 1017포항 e누리0000006000006
94P00000001080[포항]잭서프_강습경북 포항시 북구 흥해읍 영일만항로 96 2층 202호포항 e누리00000250181040084
95P00000001081[포항]잭서프_렌탈경북 포항시 북구 흥해읍 영일만항로 96 2층 202호포항 e누리0000100000001
96P00000001085[포항]요트데이_야간/성인경북 포항시 북구 두호동 1017포항 e누리0000000000000
97P00000001086[포항]요트데이_야간/청소년경북 포항시 북구 두호동 1017포항 e누리0000000000000
98P00000001087[포항]요트데이_야간/소인경북 포항시 북구 두호동 1017포항 e누리0000000000000
99P00000001088[포항]영일대 게스트하우스경북 포항시 북구 삼호로 73포항 e누리000000340020036