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

Description고지혈증 환자의 약물 처방 정보을 OMOP CDM 형식으로 생산한 데이터
Author가톨릭대학교 서울성모병원
URLhttp://cmcdata.net/data/dataset/_drug_exposure_2020-omop-cdm

Alerts

sig is highly overall correlated with drug_concept_id and 4 other fieldsHigh correlation
route_concept_id is highly overall correlated with sig and 1 other fieldsHigh correlation
route_source_value 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_concept_id is highly overall correlated with sigHigh correlation
drug_exposure_start_date is highly overall correlated with drug_exposure_idHigh correlation
drug_source_value is highly overall correlated with sig and 1 other fieldsHigh correlation
drug_type_concept_id is highly overall correlated with dose_unit_source_valueHigh correlation
dose_unit_source_value is highly overall correlated with drug_source_value and 2 other fieldsHigh correlation
drug_type_concept_id is highly imbalanced (85.9%)Imbalance
route_concept_id is highly imbalanced (85.9%)Imbalance
route_source_value is highly imbalanced (85.9%)Imbalance
drug_exposure_id has unique valuesUnique
quantity has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-10-08 18:56:16.057044
Analysis finished2023-10-08 18:56:23.672939
Duration7.62 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%
Mean4.2962801 × 108
Minimum1.4640475 × 108
Maximum6.1151466 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:23.813850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4640475 × 108
5-th percentile2.8777223 × 108
Q13.2891563 × 108
median4.4207139 × 108
Q35.1660882 × 108
95-th percentile6.0167906 × 108
Maximum6.1151466 × 108
Range4.6510991 × 108
Interquartile range (IQR)1.876932 × 108

Descriptive statistics

Standard deviation1.1057386 × 108
Coefficient of variation (CV)0.25737116
Kurtosis-0.57549692
Mean4.2962801 × 108
Median Absolute Deviation (MAD)85074904
Skewness-0.29242093
Sum4.2962801 × 1010
Variance1.2226578 × 1016
MonotonicityNot monotonic
2023-10-09T03:56:24.172697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
602807378 1
 
1.0%
146404746 1
 
1.0%
448423198 1
 
1.0%
566155786 1
 
1.0%
566127844 1
 
1.0%
364828651 1
 
1.0%
307567431 1
 
1.0%
295132517 1
 
1.0%
328407753 1
 
1.0%
474561827 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
146404746 1
1.0%
161464288 1
1.0%
193204135 1
1.0%
195404536 1
1.0%
281114043 1
1.0%
288122658 1
1.0%
291197337 1
1.0%
291819121 1
1.0%
292121843 1
1.0%
293892900 1
1.0%
ValueCountFrequency (%)
611514659 1
1.0%
611448708 1
1.0%
603504558 1
1.0%
603061306 1
1.0%
602807378 1
1.0%
601619676 1
1.0%
597750467 1
1.0%
592016312 1
1.0%
591267834 1
1.0%
570755356 1
1.0%

drug_concept_id
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25175741
Minimum1511646
Maximum43527029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:24.395080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1511646
5-th percentile1545666.8
Q11558188.2
median40163524
Q340175210
95-th percentile41150921
Maximum43527029
Range42015383
Interquartile range (IQR)38617022

Descriptive statistics

Standard deviation18642321
Coefficient of variation (CV)0.74048749
Kurtosis-1.7862264
Mean25175741
Median Absolute Deviation (MAD)11686
Skewness-0.44709464
Sum2.5175741 × 109
Variance3.4753613 × 1014
MonotonicityNot monotonic
2023-10-09T03:56:24.614717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
40175210 19
19.0%
1560305 12
12.0%
1545997 11
11.0%
1551838 8
 
8.0%
40169766 6
 
6.0%
40165245 6
 
6.0%
41150921 5
 
5.0%
40163312 5
 
5.0%
40163524 4
 
4.0%
1516800 3
 
3.0%
Other values (15) 21
21.0%
ValueCountFrequency (%)
1511646 1
 
1.0%
1516800 3
 
3.0%
1539411 1
 
1.0%
1545996 1
 
1.0%
1545997 11
11.0%
1551838 8
8.0%
1560305 12
12.0%
19123592 3
 
3.0%
21604154 1
 
1.0%
40163312 5
5.0%
ValueCountFrequency (%)
43527029 1
 
1.0%
43180979 1
 
1.0%
41150921 5
 
5.0%
40175400 1
 
1.0%
40175210 19
19.0%
40175050 3
 
3.0%
40174494 1
 
1.0%
40173602 1
 
1.0%
40169766 6
 
6.0%
40165646 1
 
1.0%

drug_exposure_start_date
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201630.48
Minimum201102
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:24.846796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201102
5-th percentile201397.5
Q1201412
median201702
Q3201805.25
95-th percentile201910
Maximum201912
Range810
Interquartile range (IQR)393.25

Descriptive statistics

Standard deviation203.18914
Coefficient of variation (CV)0.0010077303
Kurtosis-0.62222065
Mean201630.48
Median Absolute Deviation (MAD)191
Skewness-0.42673167
Sum20163048
Variance41285.828
MonotonicityNot monotonic
2023-10-09T03:56:25.100348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201412 5
 
5.0%
201707 5
 
5.0%
201403 4
 
4.0%
201904 3
 
3.0%
201405 3
 
3.0%
201706 3
 
3.0%
201702 3
 
3.0%
201806 3
 
3.0%
201608 3
 
3.0%
201807 3
 
3.0%
Other values (42) 65
65.0%
ValueCountFrequency (%)
201102 1
 
1.0%
201106 1
 
1.0%
201202 2
2.0%
201312 1
 
1.0%
201402 3
3.0%
201403 4
4.0%
201404 2
2.0%
201405 3
3.0%
201406 1
 
1.0%
201407 2
2.0%
ValueCountFrequency (%)
201912 3
3.0%
201911 1
 
1.0%
201910 2
2.0%
201909 2
2.0%
201904 3
3.0%
201903 2
2.0%
201902 1
 
1.0%
201812 1
 
1.0%
201811 1
 
1.0%
201810 1
 
1.0%

drug_type_concept_id
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
38000177
98 
44787730
 
2

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 98
98.0%
44787730 2
 
2.0%

Length

2023-10-09T03:56:25.361035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:56:25.525975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38000177 98
98.0%
44787730 2
 
2.0%

quantity
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.04
Minimum0
Maximum200
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:25.687369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.95
Q110
median20
Q350
95-th percentile160
Maximum200
Range200
Interquartile range (IQR)40

Descriptive statistics

Standard deviation49.44907
Coefficient of variation (CV)1.1762386
Kurtosis1.3766668
Mean42.04
Median Absolute Deviation (MAD)18
Skewness1.5076024
Sum4204
Variance2445.2105
MonotonicityNot monotonic
2023-10-09T03:56:25.981319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10 20
20.0%
50 12
12.0%
2 11
11.0%
20 8
 
8.0%
160 8
 
8.0%
100 5
 
5.0%
70 5
 
5.0%
4 4
 
4.0%
25 4
 
4.0%
35 4
 
4.0%
Other values (11) 19
19.0%
ValueCountFrequency (%)
0 3
 
3.0%
1 2
 
2.0%
2 11
11.0%
3 3
 
3.0%
4 4
 
4.0%
5 1
 
1.0%
10 20
20.0%
20 8
 
8.0%
25 4
 
4.0%
30 2
 
2.0%
ValueCountFrequency (%)
200 1
 
1.0%
160 8
8.0%
150 1
 
1.0%
120 1
 
1.0%
100 5
5.0%
75 2
 
2.0%
70 5
5.0%
60 1
 
1.0%
50 12
12.0%
40 2
 
2.0%

days_supply
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.23
Minimum1
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:26.182242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median50
Q3100
95-th percentile180
Maximum370
Range369
Interquartile range (IQR)99

Descriptive statistics

Standard deviation69.167759
Coefficient of variation (CV)1.0768762
Kurtosis2.4191107
Mean64.23
Median Absolute Deviation (MAD)49
Skewness1.2818845
Sum6423
Variance4784.1789
MonotonicityNot monotonic
2023-10-09T03:56:26.418019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 29
29.0%
90 10
 
10.0%
180 7
 
7.0%
120 6
 
6.0%
60 5
 
5.0%
95 4
 
4.0%
50 4
 
4.0%
15 3
 
3.0%
30 3
 
3.0%
13 3
 
3.0%
Other values (21) 26
26.0%
ValueCountFrequency (%)
1 29
29.0%
4 1
 
1.0%
6 1
 
1.0%
8 2
 
2.0%
9 2
 
2.0%
13 3
 
3.0%
15 3
 
3.0%
20 1
 
1.0%
22 1
 
1.0%
26 1
 
1.0%
ValueCountFrequency (%)
370 1
 
1.0%
200 1
 
1.0%
185 2
 
2.0%
180 7
7.0%
170 1
 
1.0%
160 1
 
1.0%
150 1
 
1.0%
140 1
 
1.0%
130 1
 
1.0%
125 2
 
2.0%

sig
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
아침 식후30분
38 
아침 식전1시간
28 
아,저 식후30분
12 
저녁 식후30분
9 
1주일에 1번 일어난 직후
8 
Other values (4)
5 

Length

Max length17
Median length8
Mean length9.02
Min length8

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row아침 식후30분
2nd row아침 식후30분
3rd row아침 식전1시간
4th row아침 식전1시간
5th row1주일에 1번 일어난 직후

Common Values

ValueCountFrequency (%)
아침 식후30분 38
38.0%
아침 식전1시간 28
28.0%
아,저 식후30분 12
 
12.0%
저녁 식후30분 9
 
9.0%
1주일에 1번 일어난 직후 8
 
8.0%
아침에 일어난 직후 복용 2
 
2.0%
IV bolus(일반) 1
 
1.0%
수액내 Mix(일반, 항암) 1
 
1.0%
1주일에 1번 아침 식후 30분 1
 
1.0%

Length

2023-10-09T03:56:26.651237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:56:26.870456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아침 67
29.9%
식후30분 59
26.3%
식전1시간 28
12.5%
아,저 12
 
5.4%
일어난 10
 
4.5%
직후 10
 
4.5%
저녁 9
 
4.0%
1주일에 9
 
4.0%
1번 9
 
4.0%
복용 2
 
0.9%
Other values (8) 9
 
4.0%

route_concept_id
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4128794
98 
4044190
 
2

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 98
98.0%
4044190 2
 
2.0%

Length

2023-10-09T03:56:27.084842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:56:27.252581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4128794 98
98.0%
4044190 2
 
2.0%

drug_source_value
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.50433 × 108
Minimum6.4000381 × 108
Maximum6.646007 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:27.421365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.4000381 × 108
5-th percentile6.4220134 × 108
Q16.4360509 × 108
median6.468011 × 108
Q36.5592653 × 108
95-th percentile6.646007 × 108
Maximum6.646007 × 108
Range24596890
Interquartile range (IQR)12321440

Descriptive statistics

Standard deviation8085768.4
Coefficient of variation (CV)0.012431363
Kurtosis-0.82847937
Mean6.50433 × 108
Median Absolute Deviation (MAD)3897235
Skewness0.81359512
Sum6.50433 × 1010
Variance6.5379651 × 1013
MonotonicityNot monotonic
2023-10-09T03:56:27.611239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
664600700 19
19.0%
646801100 12
12.0%
648900250 11
11.0%
643605090 8
 
8.0%
642201340 6
 
6.0%
642506150 6
 
6.0%
655500590 5
 
5.0%
645902470 5
 
5.0%
657801900 4
 
4.0%
645304150 3
 
3.0%
Other values (15) 21
21.0%
ValueCountFrequency (%)
640003810 1
 
1.0%
642201340 6
6.0%
642201360 3
 
3.0%
642506150 6
6.0%
642506250 2
 
2.0%
643301480 1
 
1.0%
643605090 8
8.0%
644500300 1
 
1.0%
644913470 1
 
1.0%
645000321 1
 
1.0%
ValueCountFrequency (%)
664600700 19
19.0%
657801910 1
 
1.0%
657801900 4
 
4.0%
657200450 1
 
1.0%
655501890 1
 
1.0%
655500590 5
 
5.0%
652601680 1
 
1.0%
648900270 1
 
1.0%
648900260 3
 
3.0%
648900250 11
11.0%

route_source_value
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경구약
98 
주사약
 
2

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 98
98.0%
주사약 2
 
2.0%

Length

2023-10-09T03:56:28.470780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:56:28.593276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경구약 98
98.0%
주사약 2
 
2.0%

dose_unit_source_value
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
mg
59 
mcg
28 
g
12 
T
 
1

Length

Max length3
Median length2
Mean length2.15
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
mg 59
59.0%
mcg 28
28.0%
g 12
 
12.0%
T 1
 
1.0%

Length

2023-10-09T03:56:28.741076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T03:56:28.894277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mg 59
59.0%
mcg 28
28.0%
g 12
 
12.0%
t 1
 
1.0%

Interactions

2023-10-09T03:56:21.862947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.001218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.854752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.560765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:19.560064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:20.537101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:22.022920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.109118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.975296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.684291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:19.658350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:20.750899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:22.264356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.272288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.082635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.797943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:19.871972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:20.900177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:22.512256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.454962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.239534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.932398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:20.048745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:21.113180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:22.754127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.590729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.342110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:19.324866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:20.197069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:21.347371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:22.936919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:17.723188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:18.439342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:19.445634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:20.393001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:21.645440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:56:28.993919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
drug_exposure_iddrug_concept_iddrug_exposure_start_datedrug_type_concept_idquantitydays_supplysigroute_concept_iddrug_source_valueroute_source_valuedose_unit_source_value
drug_exposure_id1.0000.0000.9960.4630.0000.2910.0000.0000.1350.0000.000
drug_concept_id0.0001.0000.0000.1900.5160.0000.7220.2950.6490.2950.444
drug_exposure_start_date0.9960.0001.0000.5160.0000.3290.0000.0000.1630.0000.458
drug_type_concept_id0.4630.1900.5161.0000.0000.0000.4270.0000.0000.0000.887
quantity0.0000.5160.0000.0001.0000.0000.7130.0000.8610.0000.723
days_supply0.2910.0000.3290.0000.0001.0000.0000.0000.0000.0000.073
sig0.0000.7220.0000.4270.7130.0001.0001.0000.8651.0000.941
route_concept_id0.0000.2950.0000.0000.0000.0001.0001.0000.0000.9190.000
drug_source_value0.1350.6490.1630.0000.8610.0000.8650.0001.0000.0000.932
route_source_value0.0000.2950.0000.0000.0000.0001.0000.9190.0001.0000.000
dose_unit_source_value0.0000.4440.4580.8870.7230.0730.9410.0000.9320.0001.000
2023-10-09T03:56:29.187306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
dose_unit_source_valuedrug_type_concept_idsigroute_concept_idroute_source_value
dose_unit_source_value1.0000.6880.8790.0000.000
drug_type_concept_id0.6881.0000.4110.0000.000
sig0.8790.4111.0000.9640.964
route_concept_id0.0000.0000.9641.0000.742
route_source_value0.0000.0000.9640.7421.000
2023-10-09T03:56:29.319962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
drug_exposure_iddrug_concept_iddrug_exposure_start_datequantitydays_supplydrug_source_valuedrug_type_concept_idsigroute_concept_idroute_source_valuedose_unit_source_value
drug_exposure_id1.000-0.0560.9520.0720.0450.1360.3410.0000.0000.0000.000
drug_concept_id-0.0561.000-0.0690.218-0.0280.3320.3110.5930.3190.3190.433
drug_exposure_start_date0.952-0.0691.0000.0760.0840.1780.3810.0000.0000.0000.286
quantity0.0720.2180.0761.000-0.0760.0180.0000.4820.0000.0000.388
days_supply0.045-0.0280.084-0.0761.0000.0110.0000.0000.0000.0000.042
drug_source_value0.1360.3320.1780.0180.0111.0000.0000.6500.0000.0000.636
drug_type_concept_id0.3410.3110.3810.0000.0000.0001.0000.4110.0000.0000.688
sig0.0000.5930.0000.4820.0000.6500.4111.0000.9640.9640.879
route_concept_id0.0000.3190.0000.0000.0000.0000.0000.9641.0000.7420.000
route_source_value0.0000.3190.0000.0000.0000.0000.0000.9640.7421.0000.000
dose_unit_source_value0.0000.4330.2860.3880.0420.6360.6880.8790.0000.0001.000

Missing values

2023-10-09T03:56:23.191736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:56:23.524704image/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
06028073784016525320140238000177201아침 식후30분4128794642506250경구약mg
119540453640165245201202380001771060아침 식후30분4128794642506150경구약mg
252826410640175210201808380001771001아침 식전1시간4128794664600700경구약mcg
34919390334017521020180138000177501아침 식전1시간4128794664600700경구약mcg
4306804135411509212014063800017770131주일에 1번 일어난 직후4128794655500590경구약mg
5419956734411509212016093800017770131주일에 1번 일어난 직후4128794655500590경구약mg
659126783440175210201909380001772560아침 식전1시간4128794664600700경구약mcg
74991761411516800201802380001773541주일에 1번 일어난 직후4128794645304150경구약mg
832584622740175210201411380001777590아침 식전1시간4128794664600700경구약mcg
95496272071516800201812380001773581주일에 1번 일어난 직후4128794645304150경구약mg
drug_exposure_iddrug_concept_iddrug_exposure_start_datedrug_type_concept_idquantitydays_supplysigroute_concept_iddrug_source_valueroute_source_valuedose_unit_source_value
90353703713154599620150638000177401아침 식후30분4128794648900270경구약mg
9128812265841150921201402380001777061주일에 1번 일어난 직후4128794655500590경구약mg
9241566003815603052016083800017721아,저 식후30분4128794646801100경구약g
93520833358401635542018063800017721아침 식후30분4128794657801910경구약mg
94441339420401752102017023800017750185아침 식전1시간4128794664600700경구약mcg
95466477415401752102017073800017775125아침 식전1시간4128794664600700경구약mcg
964585152884017521020170638000177501아침 식전1시간4128794664600700경구약mcg
9746000245215459972017063800017710100아침 식후30분4128794648900250경구약mg
9844730908143527029201703380001771090아침 식후30분4128794655501890경구약mg
99514462271401752102018053800017750200아침 식전1시간4128794664600700경구약mcg