Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory97.3 B

Variable types

Numeric6
Categorical5

Dataset

Description알코올 사용장애 환자의 약물 처방 정보를 OMOP CDM 형식으로 생산한 데이터
Author가톨릭대학교 서울성모병원
URLhttp://cmcdata.net/data/dataset/alcohol_drug_exposure_2020-omop-cdm

Alerts

drug_type_concept_id has constant value ""Constant
dose_unit_source_value has constant value ""Constant
sig is highly overall correlated with route_concept_id and 1 other fieldsHigh correlation
route_source_value is highly overall correlated with sig and 1 other fieldsHigh correlation
route_concept_id is highly overall correlated with sig and 1 other fieldsHigh correlation
drug_exposure_id is highly overall correlated with drug_exposure_start_dateHigh correlation
drug_exposure_start_date is highly overall correlated with drug_exposure_idHigh correlation
route_concept_id is highly imbalanced (71.4%)Imbalance
route_source_value is highly imbalanced (71.4%)Imbalance
drug_exposure_id has unique valuesUnique
quantity has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-10-08 18:56:46.337591
Analysis finished2023-10-08 18:56:56.772144
Duration10.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

drug_exposure_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1704814 × 108
Minimum62816939
Maximum6.1152481 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:56.956934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62816939
5-th percentile1.4891109 × 108
Q13.3245449 × 108
median4.3347802 × 108
Q35.5589722 × 108
95-th percentile6.1137118 × 108
Maximum6.1152481 × 108
Range5.4870787 × 108
Interquartile range (IQR)2.2344273 × 108

Descriptive statistics

Standard deviation1.5322563 × 108
Coefficient of variation (CV)0.36740514
Kurtosis-0.94113291
Mean4.1704814 × 108
Median Absolute Deviation (MAD)1.2131052 × 108
Skewness-0.49586872
Sum4.1704814 × 1010
Variance2.3478094 × 1016
MonotonicityNot monotonic
2023-10-09T03:56:57.243798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198586251 1
 
1.0%
381519139 1
 
1.0%
392872363 1
 
1.0%
523941317 1
 
1.0%
225999443 1
 
1.0%
491387350 1
 
1.0%
262106363 1
 
1.0%
450855114 1
 
1.0%
200040548 1
 
1.0%
176378388 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
62816939 1
1.0%
109837486 1
1.0%
140891331 1
1.0%
143145885 1
1.0%
148102269 1
1.0%
148953659 1
1.0%
155262547 1
1.0%
171493690 1
1.0%
172073847 1
1.0%
176378388 1
1.0%
ValueCountFrequency (%)
611524813 1
1.0%
611481275 1
1.0%
611475407 1
1.0%
611456112 1
1.0%
611401510 1
1.0%
611369585 1
1.0%
611367119 1
1.0%
602704768 1
1.0%
600441078 1
1.0%
598570130 1
1.0%

drug_concept_id
Real number (ℝ)

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27710980
Minimum715300
Maximum44784878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:57.940631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum715300
5-th percentile715962
Q119019113
median36270062
Q340163467
95-th percentile44784878
Maximum44784878
Range44069578
Interquartile range (IQR)21144354

Descriptive statistics

Standard deviation14972338
Coefficient of variation (CV)0.54030345
Kurtosis-0.96218482
Mean27710980
Median Absolute Deviation (MAD)8514816
Skewness-0.57780464
Sum2.771098 × 109
Variance2.241709 × 1014
MonotonicityNot monotonic
2023-10-09T03:56:58.198021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
36270062 16
16.0%
44784878 12
12.0%
40163459 11
11.0%
19019112 8
 
8.0%
19072933 7
 
7.0%
40163492 5
 
5.0%
42922843 5
 
5.0%
715962 4
 
4.0%
19112586 4
 
4.0%
19019113 3
 
3.0%
Other values (15) 25
25.0%
ValueCountFrequency (%)
715300 1
 
1.0%
715940 2
 
2.0%
715962 4
4.0%
722156 1
 
1.0%
725178 3
 
3.0%
739209 1
 
1.0%
781062 1
 
1.0%
798875 2
 
2.0%
19019112 8
8.0%
19019113 3
 
3.0%
ValueCountFrequency (%)
44784878 12
12.0%
42922843 5
 
5.0%
42480676 1
 
1.0%
40163504 2
 
2.0%
40163492 5
 
5.0%
40163459 11
11.0%
36270062 16
16.0%
35604576 2
 
2.0%
19122148 3
 
3.0%
19115249 2
 
2.0%

drug_exposure_start_date
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201603.6
Minimum200901
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:58.425367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200901
5-th percentile201103
Q1201478.75
median201612
Q3201901.25
95-th percentile201912
Maximum201912
Range1011
Interquartile range (IQR)422.5

Descriptive statistics

Standard deviation284.9499
Coefficient of variation (CV)0.0014134167
Kurtosis-0.77142649
Mean201603.6
Median Absolute Deviation (MAD)210
Skewness-0.63579135
Sum20160360
Variance81196.444
MonotonicityNot monotonic
2023-10-09T03:56:58.655381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201912 7
 
7.0%
201510 3
 
3.0%
201904 3
 
3.0%
201901 3
 
3.0%
201612 3
 
3.0%
201203 3
 
3.0%
201503 3
 
3.0%
201611 2
 
2.0%
201109 2
 
2.0%
201101 2
 
2.0%
Other values (50) 69
69.0%
ValueCountFrequency (%)
200901 1
1.0%
201004 1
1.0%
201101 2
2.0%
201103 2
2.0%
201105 1
1.0%
201109 2
2.0%
201110 1
1.0%
201112 1
1.0%
201201 1
1.0%
201202 1
1.0%
ValueCountFrequency (%)
201912 7
7.0%
201911 1
 
1.0%
201910 2
 
2.0%
201908 2
 
2.0%
201907 2
 
2.0%
201906 2
 
2.0%
201905 2
 
2.0%
201904 3
3.0%
201903 2
 
2.0%
201902 2
 
2.0%

drug_type_concept_id
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
38000177
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row38000177
2nd row38000177
3rd row38000177
4th row38000177
5th row38000177

Common Values

ValueCountFrequency (%)
38000177 100
100.0%

Length

2023-10-09T03:56:58.862403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:56:59.051551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38000177 100
100.0%

quantity
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.07
Minimum0
Maximum1332
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:59.199058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q15
median17.5
Q332
95-th percentile666
Maximum1332
Range1332
Interquartile range (IQR)27

Descriptive statistics

Standard deviation204.2007
Coefficient of variation (CV)2.6495484
Kurtosis16.733391
Mean77.07
Median Absolute Deviation (MAD)14.5
Skewness3.8889582
Sum7707
Variance41697.924
MonotonicityNot monotonic
2023-10-09T03:56:59.429324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
25 15
15.0%
1 11
11.0%
50 8
 
8.0%
5 7
 
7.0%
2 6
 
6.0%
32 6
 
6.0%
10 6
 
6.0%
666 6
 
6.0%
0 5
 
5.0%
8 5
 
5.0%
Other values (15) 25
25.0%
ValueCountFrequency (%)
0 5
5.0%
1 11
11.0%
2 6
6.0%
4 2
 
2.0%
5 7
7.0%
6 2
 
2.0%
7 1
 
1.0%
8 5
5.0%
10 6
6.0%
12 2
 
2.0%
ValueCountFrequency (%)
1332 1
 
1.0%
666 6
6.0%
333 2
 
2.0%
100 1
 
1.0%
75 1
 
1.0%
60 1
 
1.0%
50 8
8.0%
40 1
 
1.0%
34 1
 
1.0%
33 1
 
1.0%

days_supply
Real number (ℝ)

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.91
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:59.707051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median14
Q328
95-th percentile84.05
Maximum90
Range89
Interquartile range (IQR)27

Descriptive statistics

Standard deviation23.234464
Coefficient of variation (CV)1.0604502
Kurtosis1.9319478
Mean21.91
Median Absolute Deviation (MAD)13
Skewness1.5116383
Sum2191
Variance539.8403
MonotonicityNot monotonic
2023-10-09T03:57:00.023058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 27
27.0%
28 19
19.0%
14 12
12.0%
7 10
 
10.0%
35 5
 
5.0%
90 4
 
4.0%
56 4
 
4.0%
21 3
 
3.0%
15 2
 
2.0%
10 2
 
2.0%
Other values (11) 12
12.0%
ValueCountFrequency (%)
1 27
27.0%
7 10
 
10.0%
8 1
 
1.0%
10 2
 
2.0%
14 12
12.0%
15 2
 
2.0%
16 1
 
1.0%
17 1
 
1.0%
18 1
 
1.0%
21 3
 
3.0%
ValueCountFrequency (%)
90 4
4.0%
85 1
 
1.0%
84 1
 
1.0%
70 1
 
1.0%
60 1
 
1.0%
56 4
4.0%
50 1
 
1.0%
42 2
 
2.0%
35 5
5.0%
30 1
 
1.0%

sig
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
아,점,저 식후30분
35 
아침 식후30분
33 
취침전
25 
IV side
5 
아,저 식후30분
 
2

Length

Max length11
Median length10
Mean length7.79
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아침 식후30분
2nd row아,점,저 식후30분
3rd row아,점,저 식후30분
4th row아,점,저 식후30분
5th row취침전

Common Values

ValueCountFrequency (%)
아,점,저 식후30분 35
35.0%
아침 식후30분 33
33.0%
취침전 25
25.0%
IV side 5
 
5.0%
아,저 식후30분 2
 
2.0%

Length

2023-10-09T03:57:00.362522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:57:00.622424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식후30분 70
40.0%
아,점,저 35
20.0%
아침 33
18.9%
취침전 25
 
14.3%
iv 5
 
2.9%
side 5
 
2.9%
아,저 2
 
1.1%

route_concept_id
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4128794
95 
4044190
 
5

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4128794
2nd row4128794
3rd row4128794
4th row4128794
5th row4128794

Common Values

ValueCountFrequency (%)
4128794 95
95.0%
4044190 5
 
5.0%

Length

2023-10-09T03:57:01.205027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:57:01.550473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4128794 95
95.0%
4044190 5
 
5.0%

drug_source_value
Real number (ℝ)

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5328004 × 108
Minimum6.4000489 × 108
Maximum6.7080032 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:01.802735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.4000489 × 108
5-th percentile6.4200311 × 108
Q16.4530019 × 108
median6.5210064 × 108
Q36.5720108 × 108
95-th percentile6.7080012 × 108
Maximum6.7080032 × 108
Range30795430
Interquartile range (IQR)11900890

Descriptive statistics

Standard deviation9299633
Coefficient of variation (CV)0.014235293
Kurtosis-0.88163781
Mean6.5328004 × 108
Median Absolute Deviation (MAD)6700440
Skewness0.4450727
Sum6.5328004 × 1010
Variance8.6483174 × 1013
MonotonicityNot monotonic
2023-10-09T03:57:02.175039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
657201040 16
16.0%
645400200 12
12.0%
657201080 11
11.0%
642901150 8
 
8.0%
651901730 7
 
7.0%
652100640 5
 
5.0%
642901181 5
 
5.0%
668000010 4
 
4.0%
642003110 4
 
4.0%
642901160 3
 
3.0%
Other values (15) 25
25.0%
ValueCountFrequency (%)
640004890 1
 
1.0%
640004900 2
 
2.0%
642003110 4
 
4.0%
642901150 8
8.0%
642901160 3
 
3.0%
642901181 5
5.0%
645000160 2
 
2.0%
645400200 12
12.0%
648900830 1
 
1.0%
651901730 7
7.0%
ValueCountFrequency (%)
670800320 1
 
1.0%
670800310 1
 
1.0%
670800130 1
 
1.0%
670800120 3
 
3.0%
668100610 2
 
2.0%
668100550 3
 
3.0%
668000050 1
 
1.0%
668000020 2
 
2.0%
668000010 4
 
4.0%
657201080 11
11.0%

route_source_value
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경구약
95 
주사약
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경구약 95
95.0%
주사약 5
 
5.0%

Length

2023-10-09T03:57:02.519002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:57:02.794109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경구약 95
95.0%
주사약 5
 
5.0%

dose_unit_source_value
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
mg
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
mg 100
100.0%

Length

2023-10-09T03:57:02.995361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:57:03.235004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mg 100
100.0%

Interactions

2023-10-09T03:56:54.144657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.296528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.615692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.066330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:51.376502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:52.934554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:54.377734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.463642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.849707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.286354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:51.655178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.121396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:54.574320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.682081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.021063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.463766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:51.952267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.329666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:54.970392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.955709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.226291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.665559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:52.206484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.535633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:55.424226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.113538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.522089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.942173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:52.506203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.838297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:55.675492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.339523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.675030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:51.215609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:52.764819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.965015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:57:03.380627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
drug_exposure_iddrug_concept_iddrug_exposure_start_datequantitydays_supplysigroute_concept_iddrug_source_valueroute_source_value
drug_exposure_id1.0000.2620.8950.0000.0000.4940.2760.0950.276
drug_concept_id0.2621.0000.0000.1900.1400.8350.3740.7560.374
drug_exposure_start_date0.8950.0001.0000.0000.0000.3130.0000.0800.000
quantity0.0000.1900.0001.0000.4150.1810.0000.3060.000
days_supply0.0000.1400.0000.4151.0000.1790.0800.3540.080
sig0.4940.8350.3130.1810.1791.0001.0000.5961.000
route_concept_id0.2760.3740.0000.0000.0801.0001.0000.5230.986
drug_source_value0.0950.7560.0800.3060.3540.5960.5231.0000.523
route_source_value0.2760.3740.0000.0000.0801.0000.9860.5231.000
2023-10-09T03:57:03.630900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
sigroute_source_valueroute_concept_id
sig1.0000.9850.985
route_source_value0.9851.0000.894
route_concept_id0.9850.8941.000
2023-10-09T03:57:03.888951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
drug_exposure_iddrug_concept_iddrug_exposure_start_datequantitydays_supplydrug_source_valuesigroute_concept_idroute_source_value
drug_exposure_id1.000-0.1491.0000.0370.242-0.0350.2360.2040.204
drug_concept_id-0.1491.000-0.1480.399-0.262-0.0850.4660.4490.449
drug_exposure_start_date1.000-0.1481.0000.0340.240-0.0410.1730.0000.000
quantity0.0370.3990.0341.0000.1270.3930.1470.0000.000
days_supply0.242-0.2620.2400.1271.0000.1640.0980.0720.072
drug_source_value-0.035-0.085-0.0410.3930.1641.0000.4440.3560.356
sig0.2360.4660.1730.1470.0980.4441.0000.9850.985
route_concept_id0.2040.4490.0000.0000.0720.3560.9851.0000.894
route_source_value0.2040.4490.0000.0000.0720.3560.9850.8941.000

Missing values

2023-10-09T03:56:56.093557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:56:56.520958image/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

drug_exposure_iddrug_concept_iddrug_exposure_start_datedrug_type_concept_idquantitydays_supplysigroute_concept_iddrug_source_valueroute_source_valuedose_unit_source_value
019858625144784878201203380001775014아침 식후30분4128794645400200경구약mg
13722515991907293320151038000177130아,점,저 식후30분4128794651901730경구약mg
2541275990190729332018103800017711아,점,저 식후30분4128794651901730경구약mg
358208337636270062201907380001773228아,점,저 식후30분4128794657201040경구약mg
44731378444016345920170938000177251취침전4128794657201080경구약mg
533777213471594020150238000177101아침 식후30분4128794668000020경구약mg
64476545374478487820170338000177251아침 식후30분4128794645400200경구약mg
760270476836270062201911380001773370아,점,저 식후30분4128794657201040경구약mg
84142732561901911220160838000177735아,점,저 식후30분4128794642901150경구약mg
922849631772517820121038000177828취침전4128794668100550경구약mg
drug_exposure_iddrug_concept_iddrug_exposure_start_datedrug_type_concept_idquantitydays_supplysigroute_concept_iddrug_source_valueroute_source_valuedose_unit_source_value
9019428646342480676201202380001774042아침 식후30분4128794670800320경구약mg
9156558966344784878201903380001775028아침 식후30분4128794645400200경구약mg
923652230011907293320150938000177015아,점,저 식후30분4128794651901730경구약mg
9336613507036270062201509380001776667아,점,저 식후30분4128794657201040경구약mg
945459087244016345920181138000177251취침전4128794657201080경구약mg
9518754759636270062201112380001776667아,점,저 식후30분4128794657201040경구약mg
9645619171244784878201705380001775050아침 식후30분4128794645400200경구약mg
97432081378190191122016123800017717아,점,저 식후30분4128794642901150경구약mg
985263596481911258620180838000177416취침전4128794642003110경구약mg
994203823097988752016093800017701아침 식후30분4128794645000160경구약mg