Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells417
Missing cells (%)37.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 KiB
Average record size in memory100.3 B

Variable types

Numeric11

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 은평성모병원
URLhttp://cmcdata.net/data/dataset/administration-drug-data-alcohol-use-disorder-eunpyeong

Alerts

_ID is highly overall correlated with RIDHigh correlation
RID is highly overall correlated with _IDHigh correlation
ACAMPROSTATE_SCT is highly overall correlated with ACAMPROSTATE_LST and 4 other fieldsHigh correlation
ACAMPROSTATE_LST is highly overall correlated with ACAMPROSTATE_SCT and 4 other fieldsHigh correlation
NALTREXONE_SCT is highly overall correlated with ACAMPROSTATE_SCT and 5 other fieldsHigh correlation
NALTREXONE_LST is highly overall correlated with ACAMPROSTATE_SCT and 4 other fieldsHigh correlation
NALTREXONE_VALUE is highly overall correlated with LORAZEPAM_LCTHigh correlation
LORAZEPAM_SCT is highly overall correlated with ACAMPROSTATE_SCT and 4 other fieldsHigh correlation
LORAZEPAM_LCT is highly overall correlated with ACAMPROSTATE_SCT and 4 other fieldsHigh correlation
LORAZEPAM_VALUE is highly overall correlated with NALTREXONE_SCT and 1 other fieldsHigh correlation
NALTREXONE_SCT has 60 (60.0%) missing valuesMissing
NALTREXONE_LST has 60 (60.0%) missing valuesMissing
NALTREXONE_VALUE has 60 (60.0%) missing valuesMissing
LORAZEPAM_SCT has 79 (79.0%) missing valuesMissing
LORAZEPAM_LCT has 79 (79.0%) missing valuesMissing
LORAZEPAM_VALUE has 79 (79.0%) missing valuesMissing
_ID has unique valuesUnique
RID has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:56:30.992376
Analysis finished2023-10-08 18:57:00.392617
Duration29.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:00.590098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-10-09T03:57:01.271617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

RID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:01.641241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-10-09T03:57:02.000847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

ACAMPROSTATE_SCT
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20178700
Minimum20150909
Maximum20200407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:02.440276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150909
5-th percentile20150924
Q120170225
median20180956
Q320190810
95-th percentile20200210
Maximum20200407
Range49498
Interquartile range (IQR)20584.75

Descriptive statistics

Standard deviation15452.566
Coefficient of variation (CV)0.000765786
Kurtosis-0.93092042
Mean20178700
Median Absolute Deviation (MAD)10144.5
Skewness-0.48203013
Sum2.01787 × 109
Variance2.3878179 × 108
MonotonicityNot monotonic
2023-10-09T03:57:02.861026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20150924 3
 
3.0%
20190424 2
 
2.0%
20190412 2
 
2.0%
20190726 2
 
2.0%
20190812 2
 
2.0%
20170908 2
 
2.0%
20170801 2
 
2.0%
20190807 1
 
1.0%
20200216 1
 
1.0%
20190202 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
20150909 1
 
1.0%
20150915 1
 
1.0%
20150917 1
 
1.0%
20150924 3
3.0%
20150925 1
 
1.0%
20151001 1
 
1.0%
20151013 1
 
1.0%
20151026 1
 
1.0%
20151028 1
 
1.0%
20151029 1
 
1.0%
ValueCountFrequency (%)
20200407 1
1.0%
20200402 1
1.0%
20200324 1
1.0%
20200219 1
1.0%
20200216 1
1.0%
20200210 1
1.0%
20200204 1
1.0%
20200130 1
1.0%
20200121 1
1.0%
20200103 1
1.0%

ACAMPROSTATE_LST
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20182812
Minimum20151001
Maximum20200427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:03.244302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20151001
5-th percentile20151224
Q120170712
median20190304
Q320191121
95-th percentile20200407
Maximum20200427
Range49426
Interquartile range (IQR)20409

Descriptive statistics

Standard deviation14963.249
Coefficient of variation (CV)0.00074138576
Kurtosis-0.73300597
Mean20182812
Median Absolute Deviation (MAD)9959.5
Skewness-0.62545073
Sum2.0182812 × 109
Variance2.2389882 × 108
MonotonicityNot monotonic
2023-10-09T03:57:03.656796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170801 2
 
2.0%
20200407 2
 
2.0%
20191202 2
 
2.0%
20170914 2
 
2.0%
20190812 2
 
2.0%
20171208 1
 
1.0%
20190919 1
 
1.0%
20190807 1
 
1.0%
20200327 1
 
1.0%
20151230 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
20151001 1
1.0%
20151002 1
1.0%
20151013 1
1.0%
20151026 1
1.0%
20151110 1
1.0%
20151230 1
1.0%
20160106 1
1.0%
20160115 1
1.0%
20160205 1
1.0%
20160331 1
1.0%
ValueCountFrequency (%)
20200427 1
1.0%
20200420 1
1.0%
20200417 1
1.0%
20200410 1
1.0%
20200407 2
2.0%
20200403 1
1.0%
20200402 1
1.0%
20200327 1
1.0%
20200326 1
1.0%
20200324 1
1.0%

ACAMPROSTATE_VALUE
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4149.746
Minimum333
Maximum11988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:03.932775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum333
5-th percentile573.92
Q11332
median2997
Q35328
95-th percentile11988
Maximum11988
Range11655
Interquartile range (IQR)3996

Descriptive statistics

Standard deviation3480.376
Coefficient of variation (CV)0.83869615
Kurtosis0.37472037
Mean4149.746
Median Absolute Deviation (MAD)1718.6
Skewness1.1803324
Sum414974.6
Variance12113017
MonotonicityNot monotonic
2023-10-09T03:57:04.421742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1332.0 17
17.0%
2997.0 16
16.0%
5328.0 15
15.0%
11988.0 9
 
9.0%
333.0 5
 
5.0%
2484.7 1
 
1.0%
4098.5 1
 
1.0%
999.0 1
 
1.0%
2655.2 1
 
1.0%
2619.6 1
 
1.0%
Other values (33) 33
33.0%
ValueCountFrequency (%)
333.0 5
 
5.0%
586.6 1
 
1.0%
640.4 1
 
1.0%
747.5 1
 
1.0%
777.0 1
 
1.0%
903.9 1
 
1.0%
993.6 1
 
1.0%
999.0 1
 
1.0%
1224.8 1
 
1.0%
1332.0 17
17.0%
ValueCountFrequency (%)
11988.0 9
9.0%
11452.1 1
 
1.0%
10989.0 1
 
1.0%
10903.2 1
 
1.0%
10489.5 1
 
1.0%
9768.0 1
 
1.0%
7492.5 1
 
1.0%
7192.8 1
 
1.0%
6738.2 1
 
1.0%
6406.3 1
 
1.0%

NALTREXONE_SCT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)92.5%
Missing60
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean20178751
Minimum20150917
Maximum20200420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:05.217741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150917
5-th percentile20151009
Q120170760
median20180964
Q320190803
95-th percentile20191676
Maximum20200420
Range49503
Interquartile range (IQR)20043.25

Descriptive statistics

Standard deviation14813.173
Coefficient of variation (CV)0.00073409763
Kurtosis-0.73856238
Mean20178751
Median Absolute Deviation (MAD)9933.5
Skewness-0.62175492
Sum8.0715005 × 108
Variance2.1943011 × 108
MonotonicityNot monotonic
2023-10-09T03:57:06.899618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
20190412 2
 
2.0%
20171030 2
 
2.0%
20170607 2
 
2.0%
20190807 1
 
1.0%
20190503 1
 
1.0%
20180614 1
 
1.0%
20200407 1
 
1.0%
20200420 1
 
1.0%
20171115 1
 
1.0%
20190812 1
 
1.0%
Other values (27) 27
27.0%
(Missing) 60
60.0%
ValueCountFrequency (%)
20150917 1
1.0%
20150925 1
1.0%
20151013 1
1.0%
20151028 1
1.0%
20151029 1
1.0%
20160220 1
1.0%
20160415 1
1.0%
20170215 1
1.0%
20170607 2
2.0%
20170811 1
1.0%
ValueCountFrequency (%)
20200420 1
1.0%
20200407 1
1.0%
20191216 1
1.0%
20191202 1
1.0%
20191031 1
1.0%
20190920 1
1.0%
20190905 1
1.0%
20190819 1
1.0%
20190812 1
1.0%
20190807 1
1.0%

NALTREXONE_LST
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)100.0%
Missing60
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean20183202
Minimum20151110
Maximum20200427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:07.196309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20151110
5-th percentile20159665
Q120171207
median20190425
Q320190946
95-th percentile20200408
Maximum20200427
Range49317
Interquartile range (IQR)19738.5

Descriptive statistics

Standard deviation13852.153
Coefficient of variation (CV)0.00068632089
Kurtosis-0.1448259
Mean20183202
Median Absolute Deviation (MAD)9417
Skewness-0.90932666
Sum8.0732807 × 108
Variance1.9188214 × 108
MonotonicityNot monotonic
2023-10-09T03:57:07.561253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20190613 1
 
1.0%
20180710 1
 
1.0%
20200407 1
 
1.0%
20200420 1
 
1.0%
20171208 1
 
1.0%
20200312 1
 
1.0%
20190812 1
 
1.0%
20190807 1
 
1.0%
20191031 1
 
1.0%
20171204 1
 
1.0%
Other values (30) 30
30.0%
(Missing) 60
60.0%
ValueCountFrequency (%)
20151110 1
1.0%
20151119 1
1.0%
20160115 1
1.0%
20160211 1
1.0%
20160415 1
1.0%
20160824 1
1.0%
20170308 1
1.0%
20170614 1
1.0%
20171030 1
1.0%
20171204 1
1.0%
ValueCountFrequency (%)
20200427 1
1.0%
20200420 1
1.0%
20200407 1
1.0%
20200318 1
1.0%
20200312 1
1.0%
20191216 1
1.0%
20191202 1
1.0%
20191120 1
1.0%
20191031 1
1.0%
20191001 1
1.0%

NALTREXONE_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)22.5%
Missing60
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean35.79
Minimum12.5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:07.806968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.5
5-th percentile12.5
Q112.5
median50
Q350
95-th percentile50
Maximum50
Range37.5
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation17.172993
Coefficient of variation (CV)0.47982657
Kurtosis-1.6664605
Mean35.79
Median Absolute Deviation (MAD)0
Skewness-0.53731937
Sum1431.6
Variance294.91169
MonotonicityNot monotonic
2023-10-09T03:57:07.992206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
50.0 21
 
21.0%
12.5 12
 
12.0%
42.5 1
 
1.0%
45.6 1
 
1.0%
20.7 1
 
1.0%
33.9 1
 
1.0%
16.4 1
 
1.0%
37.5 1
 
1.0%
35.0 1
 
1.0%
(Missing) 60
60.0%
ValueCountFrequency (%)
12.5 12
12.0%
16.4 1
 
1.0%
20.7 1
 
1.0%
33.9 1
 
1.0%
35.0 1
 
1.0%
37.5 1
 
1.0%
42.5 1
 
1.0%
45.6 1
 
1.0%
50.0 21
21.0%
ValueCountFrequency (%)
50.0 21
21.0%
45.6 1
 
1.0%
42.5 1
 
1.0%
37.5 1
 
1.0%
35.0 1
 
1.0%
33.9 1
 
1.0%
20.7 1
 
1.0%
16.4 1
 
1.0%
12.5 12
12.0%

LORAZEPAM_SCT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing79
Missing (%)79.0%
Infinite0
Infinite (%)0.0%
Mean20175801
Minimum20160211
Maximum20200410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:08.267364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160211
5-th percentile20160325
Q120170211
median20170711
Q320181228
95-th percentile20200216
Maximum20200410
Range40199
Interquartile range (IQR)11017

Descriptive statistics

Standard deviation12556.088
Coefficient of variation (CV)0.00062233407
Kurtosis-0.49469921
Mean20175801
Median Absolute Deviation (MAD)9807
Skewness0.68429575
Sum4.2369181 × 108
Variance1.5765535 × 108
MonotonicityNot monotonic
2023-10-09T03:57:08.536208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20170526 1
 
1.0%
20181228 1
 
1.0%
20191103 1
 
1.0%
20170216 1
 
1.0%
20170211 1
 
1.0%
20190919 1
 
1.0%
20200216 1
 
1.0%
20170711 1
 
1.0%
20180608 1
 
1.0%
20160211 1
 
1.0%
Other values (11) 11
 
11.0%
(Missing) 79
79.0%
ValueCountFrequency (%)
20160211 1
1.0%
20160325 1
1.0%
20160522 1
1.0%
20160904 1
1.0%
20170204 1
1.0%
20170211 1
1.0%
20170216 1
1.0%
20170419 1
1.0%
20170526 1
1.0%
20170705 1
1.0%
ValueCountFrequency (%)
20200410 1
1.0%
20200216 1
1.0%
20191103 1
1.0%
20190919 1
1.0%
20190814 1
1.0%
20181228 1
1.0%
20180608 1
1.0%
20180127 1
1.0%
20170723 1
1.0%
20170712 1
1.0%

LORAZEPAM_LCT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing79
Missing (%)79.0%
Infinite0
Infinite (%)0.0%
Mean20178663
Minimum20160325
Maximum20200410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:08.762562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160325
5-th percentile20160522
Q120170419
median20180213
Q320190602
95-th percentile20200216
Maximum20200410
Range40085
Interquartile range (IQR)20183

Descriptive statistics

Standard deviation12153.152
Coefficient of variation (CV)0.00060227735
Kurtosis-0.7688987
Mean20178663
Median Absolute Deviation (MAD)9997
Skewness0.19073939
Sum4.2375193 × 108
Variance1.476991 × 108
MonotonicityNot monotonic
2023-10-09T03:57:09.045339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20170526 1
 
1.0%
20181228 1
 
1.0%
20191103 1
 
1.0%
20170216 1
 
1.0%
20180803 1
 
1.0%
20190919 1
 
1.0%
20200216 1
 
1.0%
20190602 1
 
1.0%
20180608 1
 
1.0%
20160529 1
 
1.0%
Other values (11) 11
 
11.0%
(Missing) 79
79.0%
ValueCountFrequency (%)
20160325 1
1.0%
20160522 1
1.0%
20160529 1
1.0%
20170204 1
1.0%
20170216 1
1.0%
20170419 1
1.0%
20170526 1
1.0%
20170705 1
1.0%
20170723 1
1.0%
20180127 1
1.0%
ValueCountFrequency (%)
20200410 1
1.0%
20200216 1
1.0%
20191103 1
1.0%
20190919 1
1.0%
20190814 1
1.0%
20190602 1
1.0%
20181228 1
1.0%
20180803 1
1.0%
20180714 1
1.0%
20180608 1
1.0%

LORAZEPAM_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)47.6%
Missing79
Missing (%)79.0%
Infinite0
Infinite (%)0.0%
Mean2.1666667
Minimum0.9
Maximum8.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:09.275363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1
Q11
median2
Q32.4
95-th percentile4
Maximum8.9
Range8
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.7720986
Coefficient of variation (CV)0.81789165
Kurtosis10.765946
Mean2.1666667
Median Absolute Deviation (MAD)1
Skewness2.9743745
Sum45.5
Variance3.1403333
MonotonicityNot monotonic
2023-10-09T03:57:09.473739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.0 7
 
7.0%
2.0 4
 
4.0%
3.0 3
 
3.0%
1.8 1
 
1.0%
8.9 1
 
1.0%
2.1 1
 
1.0%
2.4 1
 
1.0%
4.0 1
 
1.0%
1.4 1
 
1.0%
0.9 1
 
1.0%
(Missing) 79
79.0%
ValueCountFrequency (%)
0.9 1
 
1.0%
1.0 7
7.0%
1.4 1
 
1.0%
1.8 1
 
1.0%
2.0 4
4.0%
2.1 1
 
1.0%
2.4 1
 
1.0%
3.0 3
3.0%
4.0 1
 
1.0%
8.9 1
 
1.0%
ValueCountFrequency (%)
8.9 1
 
1.0%
4.0 1
 
1.0%
3.0 3
3.0%
2.4 1
 
1.0%
2.1 1
 
1.0%
2.0 4
4.0%
1.8 1
 
1.0%
1.4 1
 
1.0%
1.0 7
7.0%
0.9 1
 
1.0%

Interactions

2023-10-09T03:56:57.214168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:31.585723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:33.638112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:36.102543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:38.850097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:41.891132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:44.336721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:46.594219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.698319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:51.766468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:54.049226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:57.388927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:31.725359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:33.835304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:36.394168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:38.995551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:42.129795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:44.536182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:46.767838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.887482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:51.922067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:54.603301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:57.640445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:31.885571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:33.972330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:36.652187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:39.148113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:42.305666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:44.719324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.050896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.068401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:52.175437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:54.777210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:57.847426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:32.074659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:34.132888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:36.897220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:39.303603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:42.470141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:44.895781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.265076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.326941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:52.416919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:55.070378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:58.102018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:32.238507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:34.286910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:37.103951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:39.464095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:42.887120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:45.331530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.457810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.637042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:52.680299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:55.468649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:58.311773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:32.412620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:34.454735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:37.272104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:39.609849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:43.096697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:45.528901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.633922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.976794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:52.836501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:55.734035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:58.512712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:32.578932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:34.665036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:37.486302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:39.753752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:43.296983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:45.697802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.834037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.315195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.000941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:56.001118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:58.685228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:32.738302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:34.846087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:37.775703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:40.068514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:43.509743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:45.873559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.976206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.572481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.179392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:56.242246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:58.852580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:32.899552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:35.035392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:38.124727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:40.378647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:43.689744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:46.039318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.122177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.987576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.391211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:56.458856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:59.061144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:33.078578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:35.360960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:38.408124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:40.690651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:43.910753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:46.211749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.283167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:51.319696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.640455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:56.703871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:59.263111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:33.328518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:35.801446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:38.681688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:41.646886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:44.157449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:46.384916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.496195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:51.523026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.878870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:56.971343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:57:09.634232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
_IDRIDACAMPROSTATE_SCTACAMPROSTATE_LSTACAMPROSTATE_VALUENALTREXONE_SCTNALTREXONE_LSTNALTREXONE_VALUELORAZEPAM_SCTLORAZEPAM_LCTLORAZEPAM_VALUE
_ID1.0001.0000.3420.3070.3590.3930.0000.3070.0000.0000.000
RID1.0001.0000.3420.3070.3590.3930.0000.3070.0000.0000.000
ACAMPROSTATE_SCT0.3420.3421.0000.9390.2830.9680.8830.4610.9060.7840.830
ACAMPROSTATE_LST0.3070.3070.9391.0000.1630.8640.9510.4930.6770.6500.000
ACAMPROSTATE_VALUE0.3590.3590.2830.1631.0000.2790.4550.6240.5770.3390.000
NALTREXONE_SCT0.3930.3930.9680.8640.2791.0000.8120.0001.0000.7710.647
NALTREXONE_LST0.0000.0000.8830.9510.4550.8121.0000.0680.5980.9130.568
NALTREXONE_VALUE0.3070.3070.4610.4930.6240.0000.0681.0001.0000.4160.000
LORAZEPAM_SCT0.0000.0000.9060.6770.5771.0000.5981.0001.0000.9610.474
LORAZEPAM_LCT0.0000.0000.7840.6500.3390.7710.9130.4160.9611.0000.334
LORAZEPAM_VALUE0.0000.0000.8300.0000.0000.6470.5680.0000.4740.3341.000
2023-10-09T03:57:10.207437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
_IDRIDACAMPROSTATE_SCTACAMPROSTATE_LSTACAMPROSTATE_VALUENALTREXONE_SCTNALTREXONE_LSTNALTREXONE_VALUELORAZEPAM_SCTLORAZEPAM_LCTLORAZEPAM_VALUE
_ID1.0001.0000.041-0.0060.042-0.148-0.067-0.0850.1260.2660.048
RID1.0001.0000.041-0.0060.042-0.148-0.067-0.0850.1260.2660.048
ACAMPROSTATE_SCT0.0410.0411.0000.8600.1080.9680.888-0.2910.8960.7380.299
ACAMPROSTATE_LST-0.006-0.0060.8601.0000.1350.8810.963-0.4030.7700.7390.330
ACAMPROSTATE_VALUE0.0420.0420.1080.1351.000-0.0090.0520.2670.2980.026-0.036
NALTREXONE_SCT-0.148-0.1480.9680.881-0.0091.0000.866-0.3070.7710.5430.759
NALTREXONE_LST-0.067-0.0670.8880.9630.0520.8661.000-0.3450.8290.4860.577
NALTREXONE_VALUE-0.085-0.085-0.291-0.4030.267-0.307-0.3451.000-0.169-0.5070.144
LORAZEPAM_SCT0.1260.1260.8960.7700.2980.7710.829-0.1691.0000.8470.160
LORAZEPAM_LCT0.2660.2660.7380.7390.0260.5430.486-0.5070.8471.0000.274
LORAZEPAM_VALUE0.0480.0480.2990.330-0.0360.7590.5770.1440.1600.2741.000

Missing values

2023-10-09T03:56:59.560538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:56:59.842502image/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.
2023-10-09T03:57:00.221117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

_IDRIDACAMPROSTATE_SCTACAMPROSTATE_LSTACAMPROSTATE_VALUENALTREXONE_SCTNALTREXONE_LSTNALTREXONE_VALUELORAZEPAM_SCTLORAZEPAM_LCTLORAZEPAM_VALUE
01120190424202004031332.0201906222019071912.520190814201908142.0
122201509092020041711988.0<NA><NA><NA><NA><NA><NA>
23320190910201911204405.8201908192019112012.5<NA><NA><NA>
344201702152017030810489.5201702152017030850.0<NA><NA><NA>
45520200402202004025328.0<NA><NA><NA><NA><NA><NA>
56620151026201510261332.0<NA><NA><NA><NA><NA><NA>
6772019092720191108333.0201909052019092712.5<NA><NA><NA>
78820161018201611292456.4<NA><NA><NA>20170526201705261.0
89920200103202001032997.0<NA><NA><NA><NA><NA><NA>
9101020191105201911252997.0201912162019121612.5<NA><NA><NA>
_IDRIDACAMPROSTATE_SCTACAMPROSTATE_LSTACAMPROSTATE_VALUENALTREXONE_SCTNALTREXONE_LSTNALTREXONE_VALUELORAZEPAM_SCTLORAZEPAM_LCTLORAZEPAM_VALUE
909191201509252016011511988.0201509252016011512.5<NA><NA><NA>
91929220190517202003181332.0201904122020031812.5<NA><NA><NA>
92939320181228201812286406.3<NA><NA><NA>20181228201812280.9
93949420200219202004276290.4<NA><NA><NA><NA><NA><NA>
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