Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.8 KiB
Average record size in memory90.3 B

Variable types

Text1
Numeric9

Dataset

Description알코올 사용 장애 환자들의 스타틴 처방 데이터와 스타틴 처방 이전이나 이후에 처방된 선행 약물과 병용 약물 현황을 분석할 수 있는 데이터. 주요 처방 약물에는 가장 많이 처방되는 Acamprosate와 경구 Lorazepam, Naltrexone등이 포함됨. 이외에 수면제인 Zolpidem과 항우울제인 Mirtazapine 처방 정보가 포함됨. 약물 처방 데이터는 1일 기준 용량과 수량, 처방횟수, 처방 일수 데이터를 이용하여 총 투여량을 생성할 수 있음. 약물 처방 데이터는 RxNorm 코드로 매핑됨
Author가톨릭대학교 서울성모병원
URLhttp://cmcdata.net/data/dataset/administration-drug-data-alcohol-use-disorder

Alerts

ACAMPROSATE_SCT is highly overall correlated with ACAMPROSATE_LST and 4 other fieldsHigh correlation
ACAMPROSATE_LST is highly overall correlated with ACAMPROSATE_SCT and 4 other fieldsHigh correlation
NALTREXONE_SCT is highly overall correlated with ACAMPROSATE_SCT and 4 other fieldsHigh correlation
NALTREXONE_LST is highly overall correlated with ACAMPROSATE_SCT and 4 other fieldsHigh correlation
LORAZEPAM_SCT is highly overall correlated with ACAMPROSATE_SCT and 4 other fieldsHigh correlation
LORAZEPAM_LST is highly overall correlated with ACAMPROSATE_SCT and 4 other fieldsHigh correlation
RID has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:57:03.016740
Analysis finished2023-10-08 18:57:25.245602
Duration22.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-10-09T03:57:25.662611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
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 rowR0000040
2nd rowR0000083
3rd rowR0000280
4th rowR0000320
5th rowR0000361
ValueCountFrequency (%)
r0000040 1
 
1.0%
r0002663 1
 
1.0%
r0003006 1
 
1.0%
r0002973 1
 
1.0%
r0002954 1
 
1.0%
r0002953 1
 
1.0%
r0002950 1
 
1.0%
r0002925 1
 
1.0%
r0002877 1
 
1.0%
r0002875 1
 
1.0%
Other values (90) 90
90.0%
2023-10-09T03:57:26.402795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 360
45.0%
R 100
 
12.5%
3 58
 
7.2%
2 56
 
7.0%
5 46
 
5.8%
1 39
 
4.9%
6 33
 
4.1%
4 31
 
3.9%
7 27
 
3.4%
8 25
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
87.5%
Uppercase Letter 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 360
51.4%
3 58
 
8.3%
2 56
 
8.0%
5 46
 
6.6%
1 39
 
5.6%
6 33
 
4.7%
4 31
 
4.4%
7 27
 
3.9%
8 25
 
3.6%
9 25
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
R 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
87.5%
Latin 100
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 360
51.4%
3 58
 
8.3%
2 56
 
8.0%
5 46
 
6.6%
1 39
 
5.6%
6 33
 
4.7%
4 31
 
4.4%
7 27
 
3.9%
8 25
 
3.6%
9 25
 
3.6%
Latin
ValueCountFrequency (%)
R 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 360
45.0%
R 100
 
12.5%
3 58
 
7.2%
2 56
 
7.0%
5 46
 
5.8%
1 39
 
4.9%
6 33
 
4.1%
4 31
 
3.9%
7 27
 
3.4%
8 25
 
3.1%

ACAMPROSATE_SCT
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20132368
Minimum20090330
Maximum20180403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:26.779690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090330
5-th percentile20090801
Q120110430
median20140516
Q320150730
95-th percentile20170939
Maximum20180403
Range90073
Interquartile range (IQR)40300.25

Descriptive statistics

Standard deviation26355.082
Coefficient of variation (CV)0.00130909
Kurtosis-1.2091622
Mean20132368
Median Absolute Deviation (MAD)20195
Skewness-0.10586988
Sum2.0132368 × 109
Variance6.9459034 × 108
MonotonicityNot monotonic
2023-10-09T03:57:27.162977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20171110 2
 
2.0%
20120321 2
 
2.0%
20100604 2
 
2.0%
20090605 1
 
1.0%
20140206 1
 
1.0%
20160310 1
 
1.0%
20141002 1
 
1.0%
20140721 1
 
1.0%
20150706 1
 
1.0%
20100225 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
20090330 1
1.0%
20090420 1
1.0%
20090506 1
1.0%
20090508 1
1.0%
20090605 1
1.0%
20090811 1
1.0%
20090831 1
1.0%
20090908 1
1.0%
20091029 1
1.0%
20091214 1
1.0%
ValueCountFrequency (%)
20180403 1
1.0%
20180322 1
1.0%
20171130 1
1.0%
20171110 2
2.0%
20170930 1
1.0%
20170706 1
1.0%
20170629 1
1.0%
20170324 1
1.0%
20170321 1
1.0%
20170316 1
1.0%

ACAMPROSATE_LST
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20146497
Minimum20090420
Maximum20180723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:27.421922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090420
5-th percentile20100614
Q120121074
median20150906
Q320170824
95-th percentile20180705
Maximum20180723
Range90303
Interquartile range (IQR)49749

Descriptive statistics

Standard deviation27592.879
Coefficient of variation (CV)0.0013696117
Kurtosis-0.98292303
Mean20146497
Median Absolute Deviation (MAD)20155.5
Skewness-0.53202573
Sum2.0146497 × 109
Variance7.6136697 × 108
MonotonicityNot monotonic
2023-10-09T03:57:27.729620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20171228 2
 
2.0%
20171130 2
 
2.0%
20101116 2
 
2.0%
20180712 2
 
2.0%
20090925 1
 
1.0%
20161004 1
 
1.0%
20170406 1
 
1.0%
20140805 1
 
1.0%
20170119 1
 
1.0%
20180319 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
20090420 1
1.0%
20090925 1
1.0%
20091015 1
1.0%
20091126 1
1.0%
20100326 1
1.0%
20100629 1
1.0%
20101102 1
1.0%
20101116 2
2.0%
20101124 1
1.0%
20101207 1
1.0%
ValueCountFrequency (%)
20180723 1
1.0%
20180719 1
1.0%
20180712 2
2.0%
20180710 1
1.0%
20180705 1
1.0%
20180604 1
1.0%
20180525 1
1.0%
20180413 1
1.0%
20180319 1
1.0%
20180220 1
1.0%

ACAMPROSATE_VALUE
Real number (ℝ)

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4500.9429
Minimum731.28119
Maximum11988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:28.003958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum731.28119
5-th percentile1032.8946
Q11443.0396
median3890.0455
Q36585.7019
95-th percentile9722.0864
Maximum11988
Range11256.719
Interquartile range (IQR)5142.6623

Descriptive statistics

Standard deviation2993.7463
Coefficient of variation (CV)0.6651376
Kurtosis-0.6420874
Mean4500.9429
Median Absolute Deviation (MAD)2558.0455
Skewness0.56836881
Sum450094.29
Variance8962517.1
MonotonicityNot monotonic
2023-10-09T03:57:28.396803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1332.0 7
 
7.0%
999.0 2
 
2.0%
11988.0 2
 
2.0%
4489.55357142857 1
 
1.0%
5676.36923076923 1
 
1.0%
3094.125 1
 
1.0%
7479.6923076923 1
 
1.0%
8118.85714285714 1
 
1.0%
3678.36923076923 1
 
1.0%
3489.5625 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
731.281188118811 1
1.0%
732.6 1
1.0%
884.172413793103 1
1.0%
999.0 2
2.0%
1034.67857142857 1
1.0%
1099.22330097087 1
1.0%
1117.16129032258 1
1.0%
1129.58823529411 1
1.0%
1147.25342465753 1
1.0%
1154.91329479768 1
1.0%
ValueCountFrequency (%)
11988.0 2
2.0%
10699.4347826086 1
1.0%
10165.4516129032 1
1.0%
9793.22727272727 1
1.0%
9718.34210526315 1
1.0%
9408.57142857142 1
1.0%
9380.08421052631 1
1.0%
9324.0 1
1.0%
9240.24340770791 1
1.0%
9001.39669421487 1
1.0%

NALTREXONE_SCT
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20135769
Minimum20081119
Maximum20180417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:29.086388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20081119
5-th percentile20090906
Q120110869
median20140818
Q320153472
95-th percentile20170921
Maximum20180417
Range99298
Interquartile range (IQR)42603

Descriptive statistics

Standard deviation25577.445
Coefficient of variation (CV)0.0012702492
Kurtosis-1.0181623
Mean20135769
Median Absolute Deviation (MAD)19745
Skewness-0.30669562
Sum2.0135769 × 109
Variance6.5420569 × 108
MonotonicityNot monotonic
2023-10-09T03:57:29.690539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20171110 2
 
2.0%
20140828 2
 
2.0%
20160510 2
 
2.0%
20160310 2
 
2.0%
20150102 2
 
2.0%
20121129 1
 
1.0%
20110222 1
 
1.0%
20140721 1
 
1.0%
20160909 1
 
1.0%
20100225 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
20081119 1
1.0%
20090420 1
1.0%
20090707 1
1.0%
20090805 1
1.0%
20090818 1
1.0%
20090911 1
1.0%
20091022 1
1.0%
20100217 1
1.0%
20100225 1
1.0%
20100326 1
1.0%
ValueCountFrequency (%)
20180417 1
1.0%
20171130 1
1.0%
20171110 2
2.0%
20170930 1
1.0%
20170921 1
1.0%
20170918 1
1.0%
20170803 1
1.0%
20170731 1
1.0%
20170720 1
1.0%
20170711 1
1.0%

NALTREXONE_LST
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20145502
Minimum20090420
Maximum20180725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:30.181468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090420
5-th percentile20100516
Q120128022
median20150522
Q320170547
95-th percentile20180710
Maximum20180725
Range90305
Interquartile range (IQR)42524.5

Descriptive statistics

Standard deviation27146.577
Coefficient of variation (CV)0.0013475255
Kurtosis-0.97494926
Mean20145502
Median Absolute Deviation (MAD)20151
Skewness-0.47977924
Sum2.0145502 × 109
Variance7.3693666 × 108
MonotonicityNot monotonic
2023-10-09T03:57:30.556712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180723 2
 
2.0%
20121129 1
 
1.0%
20110428 1
 
1.0%
20161004 1
 
1.0%
20150130 1
 
1.0%
20171130 1
 
1.0%
20140805 1
 
1.0%
20170119 1
 
1.0%
20180319 1
 
1.0%
20180724 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
20090420 1
1.0%
20090925 1
1.0%
20091126 1
1.0%
20100217 1
1.0%
20100225 1
1.0%
20100531 1
1.0%
20100629 1
1.0%
20101005 1
1.0%
20101102 1
1.0%
20101110 1
1.0%
ValueCountFrequency (%)
20180725 1
1.0%
20180724 1
1.0%
20180723 2
2.0%
20180712 1
1.0%
20180710 1
1.0%
20180705 1
1.0%
20180702 1
1.0%
20180612 1
1.0%
20180515 1
1.0%
20180326 1
1.0%

NALTREXONE_VALUE
Real number (ℝ)

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.657853
Minimum12.5
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:30.784161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.5
5-th percentile12.5
Q138.210938
median50
Q350
95-th percentile148.1688
Maximum200
Range187.5
Interquartile range (IQR)11.789062

Descriptive statistics

Standard deviation40.063376
Coefficient of variation (CV)0.73298481
Kurtosis3.4405501
Mean54.657853
Median Absolute Deviation (MAD)5.0964096
Skewness1.833414
Sum5465.7853
Variance1605.0741
MonotonicityNot monotonic
2023-10-09T03:57:31.087380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.0 32
32.0%
12.5 18
18.0%
45.3125 3
 
3.0%
37.5 2
 
2.0%
152.220459952418 1
 
1.0%
48.8916256157635 1
 
1.0%
49.9119718309859 1
 
1.0%
51.0663507109004 1
 
1.0%
200.0 1
 
1.0%
71.2155963302752 1
 
1.0%
Other values (39) 39
39.0%
ValueCountFrequency (%)
12.5 18
18.0%
14.6844660194174 1
 
1.0%
24.8113207547169 1
 
1.0%
31.25 1
 
1.0%
37.5 2
 
2.0%
37.8802281368821 1
 
1.0%
38.0 1
 
1.0%
38.28125 1
 
1.0%
40.6528189910979 1
 
1.0%
41.1290322580645 1
 
1.0%
ValueCountFrequency (%)
200.0 1
1.0%
191.39344262295 1
1.0%
180.6640625 1
1.0%
152.220459952418 1
1.0%
150.284738041002 1
1.0%
148.057432432432 1
1.0%
147.767857142857 1
1.0%
141.111111111111 1
1.0%
128.056167400881 1
1.0%
127.151335311572 1
1.0%

LORAZEPAM_SCT
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20139061
Minimum20090330
Maximum20180417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:31.429829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090330
5-th percentile20090830
Q120117993
median20141056
Q320160536
95-th percentile20180205
Maximum20180417
Range90087
Interquartile range (IQR)42543.25

Descriptive statistics

Standard deviation26626.892
Coefficient of variation (CV)0.0013221516
Kurtosis-0.99565285
Mean20139061
Median Absolute Deviation (MAD)20012.5
Skewness-0.34524942
Sum2.0139061 × 109
Variance7.089914 × 108
MonotonicityNot monotonic
2023-10-09T03:57:31.920356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20171110 2
 
2.0%
20100326 1
 
1.0%
20140206 1
 
1.0%
20160310 1
 
1.0%
20161019 1
 
1.0%
20140721 1
 
1.0%
20160909 1
 
1.0%
20100225 1
 
1.0%
20151223 1
 
1.0%
20141001 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
20090330 1
1.0%
20090420 1
1.0%
20090512 1
1.0%
20090605 1
1.0%
20090819 1
1.0%
20090831 1
1.0%
20091214 1
1.0%
20100225 1
1.0%
20100326 1
1.0%
20100609 1
1.0%
ValueCountFrequency (%)
20180417 1
1.0%
20180414 1
1.0%
20180329 1
1.0%
20180326 1
1.0%
20180212 1
1.0%
20180205 1
1.0%
20171222 1
1.0%
20171130 1
1.0%
20171110 2
2.0%
20170930 1
1.0%

LORAZEPAM_LST
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20144294
Minimum20090330
Maximum20180605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:32.322249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090330
5-th percentile20090908
Q120127952
median20150670
Q320170206
95-th percentile20180218
Maximum20180605
Range90275
Interquartile range (IQR)42253.75

Descriptive statistics

Standard deviation26708.461
Coefficient of variation (CV)0.0013258574
Kurtosis-0.8007269
Mean20144294
Median Absolute Deviation (MAD)19944.5
Skewness-0.57438638
Sum2.0144294 × 109
Variance7.1334187 × 108
MonotonicityNot monotonic
2023-10-09T03:57:32.586357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20171110 2
 
2.0%
20110929 1
 
1.0%
20151109 1
 
1.0%
20160310 1
 
1.0%
20161019 1
 
1.0%
20140721 1
 
1.0%
20160909 1
 
1.0%
20131023 1
 
1.0%
20151223 1
 
1.0%
20150107 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
20090330 1
1.0%
20090420 1
1.0%
20090512 1
1.0%
20090605 1
1.0%
20090831 1
1.0%
20090912 1
1.0%
20100610 1
1.0%
20100722 1
1.0%
20100817 1
1.0%
20100924 1
1.0%
ValueCountFrequency (%)
20180605 1
1.0%
20180604 1
1.0%
20180417 1
1.0%
20180329 1
1.0%
20180326 1
1.0%
20180212 1
1.0%
20180205 1
1.0%
20171228 1
1.0%
20171222 1
1.0%
20171218 1
1.0%

LORAZEPAM_VALUE
Real number (ℝ)

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4828533
Minimum1.175
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:32.882098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.175
5-th percentile1.6407143
Q12
median2
Q32.3453819
95-th percentile4.3086991
Maximum8
Range6.825
Interquartile range (IQR)0.3453819

Descriptive statistics

Standard deviation1.1791982
Coefficient of variation (CV)0.47493674
Kurtosis9.5459365
Mean2.4828533
Median Absolute Deviation (MAD)0
Skewness2.9397712
Sum248.28533
Variance1.3905085
MonotonicityNot monotonic
2023-10-09T03:57:33.191918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2.0 51
51.0%
4.0 7
 
7.0%
2.66666666666666 2
 
2.0%
3.0 2
 
2.0%
1.78571428571428 2
 
2.0%
2.28571428571428 1
 
1.0%
2.18181818181818 1
 
1.0%
4.5603448275862 1
 
1.0%
2.64285714285714 1
 
1.0%
1.4 1
 
1.0%
Other values (31) 31
31.0%
ValueCountFrequency (%)
1.175 1
 
1.0%
1.36842105263157 1
 
1.0%
1.4 1
 
1.0%
1.46153846153846 1
 
1.0%
1.6 1
 
1.0%
1.64285714285714 1
 
1.0%
1.78571428571428 2
 
2.0%
1.96268656716417 1
 
1.0%
2.0 51
51.0%
2.025 1
 
1.0%
ValueCountFrequency (%)
8.0 1
 
1.0%
7.64705882352941 1
 
1.0%
6.96666666666666 1
 
1.0%
6.0 1
 
1.0%
4.5603448275862 1
 
1.0%
4.29545454545454 1
 
1.0%
4.0 7
7.0%
3.86666666666666 1
 
1.0%
3.14285714285714 1
 
1.0%
3.0 2
 
2.0%

Interactions

2023-10-09T03:57:23.058511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.116950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.815927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:10.675420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:12.822179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:15.670465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.454925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:19.774001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.404058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.203259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.341695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.957835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:10.849441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:13.185033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.017540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.001177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:19.969138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.598694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.373123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.564452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:09.123912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.012053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:13.531849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.298623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.162518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.138903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.740781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.556448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.727272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:09.298769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.213314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:13.909180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.477634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.327109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.327144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.968528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.751801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.881826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:09.463145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.373670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:14.113606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.604882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.513638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.475137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:22.209565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.904976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.028082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:09.608860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.530643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:14.253963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.802514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.663487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.706340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:22.366246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:24.054747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.223070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:09.841256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.964887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:14.584625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.967281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.821594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.859180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:22.515216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:24.247526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.440781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:10.201064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:12.273733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:14.905484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.126781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.977778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.023466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:22.665850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:24.395192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.646340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:10.475509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:12.427862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:15.229864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.278495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:19.440769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.236800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:22.865006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:57:33.539994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDACAMPROSATE_SCTACAMPROSATE_LSTACAMPROSATE_VALUENALTREXONE_SCTNALTREXONE_LSTNALTREXONE_VALUELORAZEPAM_SCTLORAZEPAM_LSTLORAZEPAM_VALUE
RID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ACAMPROSATE_SCT1.0001.0000.9110.0000.9320.9090.0000.9670.9230.236
ACAMPROSATE_LST1.0000.9111.0000.0000.9210.9730.2840.9390.9610.319
ACAMPROSATE_VALUE1.0000.0000.0001.0000.2810.2030.5880.0000.0000.100
NALTREXONE_SCT1.0000.9320.9210.2811.0000.9110.2870.9280.8880.350
NALTREXONE_LST1.0000.9090.9730.2030.9111.0000.5210.9270.9370.375
NALTREXONE_VALUE1.0000.0000.2840.5880.2870.5211.0000.2230.0000.000
LORAZEPAM_SCT1.0000.9670.9390.0000.9280.9270.2231.0000.9760.365
LORAZEPAM_LST1.0000.9230.9610.0000.8880.9370.0000.9761.0000.367
LORAZEPAM_VALUE1.0000.2360.3190.1000.3500.3750.0000.3650.3671.000
2023-10-09T03:57:33.975936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACAMPROSATE_SCTACAMPROSATE_LSTACAMPROSATE_VALUENALTREXONE_SCTNALTREXONE_LSTNALTREXONE_VALUELORAZEPAM_SCTLORAZEPAM_LSTLORAZEPAM_VALUE
ACAMPROSATE_SCT1.0000.6790.2280.9170.668-0.1120.8100.681-0.113
ACAMPROSATE_LST0.6791.0000.3060.7260.8710.0460.7070.781-0.077
ACAMPROSATE_VALUE0.2280.3061.0000.2910.2700.1390.2530.299-0.125
NALTREXONE_SCT0.9170.7260.2911.0000.716-0.0870.7810.730-0.165
NALTREXONE_LST0.6680.8710.2700.7161.000-0.0430.7030.730-0.065
NALTREXONE_VALUE-0.1120.0460.139-0.087-0.0431.000-0.0850.0210.083
LORAZEPAM_SCT0.8100.7070.2530.7810.703-0.0851.0000.890-0.090
LORAZEPAM_LST0.6810.7810.2990.7300.7300.0210.8901.000-0.058
LORAZEPAM_VALUE-0.113-0.077-0.125-0.165-0.0650.083-0.090-0.0581.000

Missing values

2023-10-09T03:57:24.693628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:57:25.086404image/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

RIDACAMPROSATE_SCTACAMPROSATE_LSTACAMPROSATE_VALUENALTREXONE_SCTNALTREXONE_LSTNALTREXONE_VALUELORAZEPAM_SCTLORAZEPAM_LSTLORAZEPAM_VALUE
0R000004020121129201211293256.02012112920121129107.14285720121129201211292.0
1R000008320110717201110181166.935345201107172011101848.52941220110717201107172.857143
2R000028020170120201701203784.090909201701202017012012.520170120201701202.142857
3R000032020140808201408281332.0201408282014082812.520140728201407282.0
4R000036120151209201512096140.195122201512092015120950.020151209201512092.0
5R000037720150527201708306677.781553201707202017083049.062520150527201707201.962687
6R000039020101021201205221633.229682201106292011062950.020110525201203273.142857
7R000046520110628201106283759.22011062820110628147.76785720110628201106282.190476
8R000052820170321201703211117.16129201703212017032112.520170321201703212.0
9R000053720150225201505081219.812834201502252018072524.81132120150225201502252.136364
RIDACAMPROSATE_SCTACAMPROSATE_LSTACAMPROSATE_VALUENALTREXONE_SCTNALTREXONE_LSTNALTREXONE_VALUELORAZEPAM_SCTLORAZEPAM_LSTLORAZEPAM_VALUE
90R000351320140617201802209793.227273201407252018061240.65281920140725201407256.966667
91R000352420151127201807121895.280242201602112018071267.59920620160721201701252.0
92R000353820090831201108165820.888087200908052010053150.020090831200908312.0
93R000355020150528201710104691.355372201506122017052558.43128420150925201710102.083333
94R0003575201502272015053110165.451613201410292015053141.12903220150531201702192.320755
95R000359220121213201402131154.913295201402132014021312.520131029201402133.866667
96R000362520130510201305301332.0201305302013053012.520130530201305302.0
97R000366620130409201306132930.4201305232013052350.020130523201305231.642857
98R000367920120221201304264920.816832201302282013042350.020120520201304262.0
99R000368420110502201301141332.0201302132015113012.520120629201206292.0