Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells350
Missing cells (%)26.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory111.3 B

Variable types

Text1
DateTime6
Numeric6

Dataset

Description당뇨 환자 중 DPP4i (RxNorm 코드:19125041, 40239218, 43013911, 43013924, 42960599, 42961500) 처방 기록이 있는 환자의 Metformin(19106521, 40164929, 40164946, 40164897, 40164894, 40164925)과 Insulin(46234044, 35782236, 35779361, 41348914, 35786039, 36809748, 42920572, 46234044, 41370419, 41349142, 46234044, 35782557, 35159339, 35781503, 35781503, 46234044, 46234044, 586875, 35781503, 35781503, 46234044, 41348508, 40717097 , 35779506, 40755064, 42921713)처방 기록. 최초 처방일과 최종처방일 데이터로 선행 및 병용 여부를 확인 할 수 있음.
Author가톨릭대학교 서울성모병원
URLhttp://cmcdata.net/data/dataset/diabetes_medication

Alerts

DPP4i_f_prcd is highly overall correlated with DPP4i_l_prcdHigh correlation
DPP4i_l_prcd is highly overall correlated with DPP4i_f_prcdHigh correlation
Met_f_date has 21 (21.0%) missing valuesMissing
Met_f_prcd has 21 (21.0%) missing valuesMissing
Met_l_date has 27 (27.0%) missing valuesMissing
Met_l_prcd has 27 (27.0%) missing valuesMissing
DPP4i_l_date has 14 (14.0%) missing valuesMissing
DPP4i_l_prcd has 14 (14.0%) missing valuesMissing
Insul_f_date has 53 (53.0%) missing valuesMissing
Insul_f_prcd has 53 (53.0%) missing valuesMissing
Insul_l_date has 60 (60.0%) missing valuesMissing
Insul_l_prcd has 60 (60.0%) missing valuesMissing
RID has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:55:56.714207
Analysis finished2023-10-08 18:56:08.432896
Duration11.72 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
2023-10-09T03:56:08.958387image/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 rowR0000001
2nd rowR0000002
3rd rowR0000003
4th rowR0000004
5th rowR0000005
ValueCountFrequency (%)
r0000001 1
 
1.0%
r0000063 1
 
1.0%
r0000074 1
 
1.0%
r0000073 1
 
1.0%
r0000072 1
 
1.0%
r0000071 1
 
1.0%
r0000070 1
 
1.0%
r0000069 1
 
1.0%
r0000068 1
 
1.0%
r0000067 1
 
1.0%
Other values (90) 90
90.0%
2023-10-09T03:56:09.854712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 519
64.9%
R 100
 
12.5%
1 21
 
2.6%
3 20
 
2.5%
4 20
 
2.5%
5 20
 
2.5%
6 20
 
2.5%
7 20
 
2.5%
8 20
 
2.5%
9 20
 
2.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 519
74.1%
1 21
 
3.0%
3 20
 
2.9%
4 20
 
2.9%
5 20
 
2.9%
6 20
 
2.9%
7 20
 
2.9%
8 20
 
2.9%
9 20
 
2.9%
2 20
 
2.9%
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 (%)
0 519
74.1%
1 21
 
3.0%
3 20
 
2.9%
4 20
 
2.9%
5 20
 
2.9%
6 20
 
2.9%
7 20
 
2.9%
8 20
 
2.9%
9 20
 
2.9%
2 20
 
2.9%
Latin
ValueCountFrequency (%)
R 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 519
64.9%
R 100
 
12.5%
1 21
 
2.6%
3 20
 
2.5%
4 20
 
2.5%
5 20
 
2.5%
6 20
 
2.5%
7 20
 
2.5%
8 20
 
2.5%
9 20
 
2.5%

Met_f_date
Date

MISSING 

Distinct44
Distinct (%)55.7%
Missing21
Missing (%)21.0%
Memory size932.0 B
Minimum2009-07-01 00:00:00
Maximum2019-04-01 00:00:00
2023-10-09T03:56:10.243889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:10.659500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

Met_f_prcd
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)7.6%
Missing21
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean38298991
Minimum19106521
Maximum40164946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:10.952076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19106521
5-th percentile19106521
Q140164911
median40164925
Q340164929
95-th percentile40164946
Maximum40164946
Range21058425
Interquartile range (IQR)18

Descriptive statistics

Standard deviation6022542.8
Coefficient of variation (CV)0.15725069
Kurtosis6.8861114
Mean38298991
Median Absolute Deviation (MAD)4
Skewness-2.9516728
Sum3.0256203 × 109
Variance3.6271022 × 1013
MonotonicityNot monotonic
2023-10-09T03:56:11.234340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40164925 22
22.0%
40164929 19
19.0%
40164946 18
18.0%
40164897 9
9.0%
19106521 7
 
7.0%
40164894 4
 
4.0%
(Missing) 21
21.0%
ValueCountFrequency (%)
19106521 7
 
7.0%
40164894 4
 
4.0%
40164897 9
9.0%
40164925 22
22.0%
40164929 19
19.0%
40164946 18
18.0%
ValueCountFrequency (%)
40164946 18
18.0%
40164929 19
19.0%
40164925 22
22.0%
40164897 9
9.0%
40164894 4
 
4.0%
19106521 7
 
7.0%

Met_l_date
Date

MISSING 

Distinct42
Distinct (%)57.5%
Missing27
Missing (%)27.0%
Memory size932.0 B
Minimum2010-09-01 00:00:00
Maximum2019-06-01 00:00:00
2023-10-09T03:56:11.577217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:11.911833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)

Met_l_prcd
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)8.2%
Missing27
Missing (%)27.0%
Infinite0
Infinite (%)0.0%
Mean39299512
Minimum19106521
Maximum40164946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:12.170393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19106521
5-th percentile40164894
Q140164897
median40164929
Q340164946
95-th percentile40164946
Maximum40164946
Range21058425
Interquartile range (IQR)49

Descriptive statistics

Standard deviation4209276.4
Coefficient of variation (CV)0.1071076
Kurtosis20.858821
Mean39299512
Median Absolute Deviation (MAD)17
Skewness-4.7210053
Sum2.8688644 × 109
Variance1.7718008 × 1013
MonotonicityNot monotonic
2023-10-09T03:56:12.349008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
40164946 22
22.0%
40164929 21
21.0%
40164925 11
11.0%
40164897 9
 
9.0%
40164894 7
 
7.0%
19106521 3
 
3.0%
(Missing) 27
27.0%
ValueCountFrequency (%)
19106521 3
 
3.0%
40164894 7
 
7.0%
40164897 9
9.0%
40164925 11
11.0%
40164929 21
21.0%
40164946 22
22.0%
ValueCountFrequency (%)
40164946 22
22.0%
40164929 21
21.0%
40164925 11
11.0%
40164897 9
9.0%
40164894 7
 
7.0%
19106521 3
 
3.0%

DPP4i_f_date
Date

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2009-07-01 00:00:00
Maximum2019-05-01 00:00:00
2023-10-09T03:56:12.574327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:12.894526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DPP4i_f_prcd
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34157093
Minimum19125041
Maximum43013924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:13.132971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19125041
5-th percentile19125041
Q119125041
median40239218
Q342961500
95-th percentile43013924
Maximum43013924
Range23888883
Interquartile range (IQR)23836459

Descriptive statistics

Standard deviation10898510
Coefficient of variation (CV)0.31907018
Kurtosis-1.5609574
Mean34157093
Median Absolute Deviation (MAD)2722282
Skewness-0.65414465
Sum3.4157093 × 109
Variance1.1877752 × 1014
MonotonicityNot monotonic
2023-10-09T03:56:13.326227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
19125041 34
34.0%
40239218 26
26.0%
42961500 16
16.0%
43013924 11
 
11.0%
42960599 9
 
9.0%
43013911 4
 
4.0%
ValueCountFrequency (%)
19125041 34
34.0%
40239218 26
26.0%
42960599 9
 
9.0%
42961500 16
16.0%
43013911 4
 
4.0%
43013924 11
 
11.0%
ValueCountFrequency (%)
43013924 11
 
11.0%
43013911 4
 
4.0%
42961500 16
16.0%
42960599 9
 
9.0%
40239218 26
26.0%
19125041 34
34.0%

DPP4i_l_date
Date

MISSING 

Distinct43
Distinct (%)50.0%
Missing14
Missing (%)14.0%
Memory size932.0 B
Minimum2009-12-01 00:00:00
Maximum2019-06-01 00:00:00
2023-10-09T03:56:13.565617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:13.853917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)

DPP4i_l_prcd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)7.0%
Missing14
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean35589831
Minimum19125041
Maximum43013924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:14.058258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19125041
5-th percentile19125041
Q119125041
median40239218
Q342961500
95-th percentile43013924
Maximum43013924
Range23888883
Interquartile range (IQR)23836459

Descriptive statistics

Standard deviation10365872
Coefficient of variation (CV)0.29125938
Kurtosis-1.0412416
Mean35589831
Median Absolute Deviation (MAD)2722282
Skewness-0.96318277
Sum3.0607255 × 109
Variance1.074513 × 1014
MonotonicityNot monotonic
2023-10-09T03:56:14.322117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
19125041 24
24.0%
40239218 23
23.0%
42961500 14
14.0%
42960599 11
11.0%
43013924 11
11.0%
43013911 3
 
3.0%
(Missing) 14
14.0%
ValueCountFrequency (%)
19125041 24
24.0%
40239218 23
23.0%
42960599 11
11.0%
42961500 14
14.0%
43013911 3
 
3.0%
43013924 11
11.0%
ValueCountFrequency (%)
43013924 11
11.0%
43013911 3
 
3.0%
42961500 14
14.0%
42960599 11
11.0%
40239218 23
23.0%
19125041 24
24.0%

Insul_f_date
Date

MISSING 

Distinct35
Distinct (%)74.5%
Missing53
Missing (%)53.0%
Memory size932.0 B
Minimum2009-07-01 00:00:00
Maximum2019-04-01 00:00:00
2023-10-09T03:56:14.509633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:14.808257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

Insul_f_prcd
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)14.9%
Missing53
Missing (%)53.0%
Infinite0
Infinite (%)0.0%
Mean36102020
Minimum586875
Maximum46234044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:15.080968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum586875
5-th percentile35779361
Q135779361
median35781503
Q335781503
95-th percentile41348914
Maximum46234044
Range45647169
Interquartile range (IQR)2142

Descriptive statistics

Standard deviation5804152.7
Coefficient of variation (CV)0.16077086
Kurtosis31.849968
Mean36102020
Median Absolute Deviation (MAD)2142
Skewness-4.9737902
Sum1.6967949 × 109
Variance3.3688189 × 1013
MonotonicityNot monotonic
2023-10-09T03:56:15.285760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
35781503 20
 
20.0%
35779361 16
 
16.0%
41348914 5
 
5.0%
40755064 2
 
2.0%
36809748 2
 
2.0%
586875 1
 
1.0%
46234044 1
 
1.0%
(Missing) 53
53.0%
ValueCountFrequency (%)
586875 1
 
1.0%
35779361 16
16.0%
35781503 20
20.0%
36809748 2
 
2.0%
40755064 2
 
2.0%
41348914 5
 
5.0%
46234044 1
 
1.0%
ValueCountFrequency (%)
46234044 1
 
1.0%
41348914 5
 
5.0%
40755064 2
 
2.0%
36809748 2
 
2.0%
35781503 20
20.0%
35779361 16
16.0%
586875 1
 
1.0%

Insul_l_date
Date

MISSING 

Distinct24
Distinct (%)60.0%
Missing60
Missing (%)60.0%
Memory size932.0 B
Minimum2009-12-01 00:00:00
Maximum2019-06-01 00:00:00
2023-10-09T03:56:15.504444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:15.694770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

Insul_l_prcd
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)25.0%
Missing60
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean35888912
Minimum586875
Maximum42920572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:15.905668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum586875
5-th percentile35159339
Q135781004
median35781503
Q335786039
95-th percentile41349142
Maximum42920572
Range42333697
Interquartile range (IQR)5035.25

Descriptive statistics

Standard deviation6158600.1
Coefficient of variation (CV)0.17160175
Kurtosis29.206161
Mean35888912
Median Absolute Deviation (MAD)2069.5
Skewness-4.9305402
Sum1.4355565 × 109
Variance3.7928355 × 1013
MonotonicityNot monotonic
2023-10-09T03:56:16.149038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
35781503 19
 
19.0%
35159339 5
 
5.0%
41349142 5
 
5.0%
35779361 3
 
3.0%
36809748 2
 
2.0%
35786039 2
 
2.0%
41348914 1
 
1.0%
35779506 1
 
1.0%
586875 1
 
1.0%
42920572 1
 
1.0%
(Missing) 60
60.0%
ValueCountFrequency (%)
586875 1
 
1.0%
35159339 5
 
5.0%
35779361 3
 
3.0%
35779506 1
 
1.0%
35781503 19
19.0%
35786039 2
 
2.0%
36809748 2
 
2.0%
41348914 1
 
1.0%
41349142 5
 
5.0%
42920572 1
 
1.0%
ValueCountFrequency (%)
42920572 1
 
1.0%
41349142 5
 
5.0%
41348914 1
 
1.0%
36809748 2
 
2.0%
35786039 2
 
2.0%
35781503 19
19.0%
35779506 1
 
1.0%
35779361 3
 
3.0%
35159339 5
 
5.0%
586875 1
 
1.0%

Interactions

2023-10-09T03:56:04.513104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:58.621340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:00.412004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.547814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.535372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.590668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:04.709853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:59.003519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:00.659212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.698107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.711794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.754442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:05.100314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:59.385393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:00.831878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.849629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.894836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.940041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:05.280470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:59.567207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.010647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.035323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.016893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:04.083096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:05.491843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:59.837214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.228237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.188077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.203458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:04.244870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:06.140377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:00.082078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.351410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.312455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.427620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:04.373090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:56:16.425828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDMet_f_dateMet_f_prcdMet_l_dateMet_l_prcdDPP4i_f_dateDPP4i_f_prcdDPP4i_l_dateDPP4i_l_prcdInsul_f_dateInsul_f_prcdInsul_l_dateInsul_l_prcd
RID1.0001.000NaN1.000NaN1.0001.0001.0001.0001.0001.0001.0001.000
Met_f_date1.0001.000NaN0.000NaN0.9620.1120.0000.7010.9440.9200.0000.741
Met_f_prcdNaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Met_l_date1.0000.000NaN1.000NaN0.5940.4920.9680.5860.7630.2890.9461.000
Met_l_prcdNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaN
DPP4i_f_date1.0000.962NaN0.594NaN1.0000.4840.9250.6120.9710.7780.9470.797
DPP4i_f_prcd1.0000.112NaN0.492NaN0.4841.0000.3760.8660.2960.0000.1870.000
DPP4i_l_date1.0000.000NaN0.968NaN0.9250.3761.0000.2900.8350.8010.9580.000
DPP4i_l_prcd1.0000.701NaN0.586NaN0.6120.8660.2901.0000.4690.0000.1460.355
Insul_f_date1.0000.944NaN0.763NaN0.9710.2960.8350.4691.0000.8360.9350.850
Insul_f_prcd1.0000.920NaN0.289NaN0.7780.0000.8010.0000.8361.0000.9020.000
Insul_l_date1.0000.000NaN0.946NaN0.9470.1870.9580.1460.9350.9021.0000.419
Insul_l_prcd1.0000.741NaN1.000NaN0.7970.0000.0000.3550.8500.0000.4191.000
2023-10-09T03:56:16.712175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Met_f_prcdMet_l_prcdDPP4i_f_prcdDPP4i_l_prcdInsul_f_prcdInsul_l_prcd
Met_f_prcd1.0000.4630.1280.1350.1990.028
Met_l_prcd0.4631.0000.0680.0160.4240.064
DPP4i_f_prcd0.1280.0681.0000.674-0.0180.250
DPP4i_l_prcd0.1350.0160.6741.000-0.0240.005
Insul_f_prcd0.1990.424-0.018-0.0241.000-0.162
Insul_l_prcd0.0280.0640.2500.005-0.1621.000

Missing values

2023-10-09T03:56:06.501810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:56:07.382591image/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.
2023-10-09T03:56:07.926270image/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

RIDMet_f_dateMet_f_prcdMet_l_dateMet_l_prcdDPP4i_f_dateDPP4i_f_prcdDPP4i_l_dateDPP4i_l_prcdInsul_f_dateInsul_f_prcdInsul_l_dateInsul_l_prcd
0R00000012014-02401649462015-02401649462014-02402392182015-0240239218<NA><NA><NA><NA>
1R0000002<NA><NA><NA><NA>2017-12402392182019-0340239218<NA><NA><NA><NA>
2R00000032010-07401649292015-08401648972010-11191250412015-0819125041<NA><NA><NA><NA>
3R00000042015-08191065212016-11401649292016-1142960599<NA><NA><NA><NA><NA><NA>
4R00000052014-03401649252016-01401649252014-0340239218<NA><NA><NA><NA><NA><NA>
5R00000062009-09401649462019-01401649462017-04429605992019-0540239218<NA><NA><NA><NA>
6R00000072009-07401648972014-07401648972013-12429615002014-0742961500<NA><NA><NA><NA>
7R00000082010-07401649462018-05401649292014-10402392182018-11402392182018-01357815032019-0535159339
8R00000092014-09401649462018-08401649462014-09402392182018-08402392182014-0941348914<NA><NA>
9R00000102012-05401649292019-06401648972013-02402392182015-03402392182013-12357793612019-0641349142
RIDMet_f_dateMet_f_prcdMet_l_dateMet_l_prcdDPP4i_f_dateDPP4i_f_prcdDPP4i_l_dateDPP4i_l_prcdInsul_f_dateInsul_f_prcdInsul_l_dateInsul_l_prcd
90R0000091<NA><NA><NA><NA>2017-05429605992017-12402392182017-04357815032017-1235781503
91R00000922012-08401649292017-02401649252016-11429605992017-02429605992012-08357793612014-0435779361
92R00000932010-01401649252011-07401649292010-01191250412010-03191250412011-07357815032012-1035781503
93R00000942016-0340164925<NA><NA>2016-03429615002017-12429615002009-07357793612019-0435159339
94R0000095<NA><NA><NA><NA>2017-08430139242018-03430139242017-0835781503<NA><NA>
95R00000962014-10401649292019-06401648942017-09402392182018-0642961500<NA><NA><NA><NA>
96R00000972016-07401649252017-04401649252011-09191250412017-0419125041<NA><NA><NA><NA>
97R00000982009-07401649252018-03401648972009-07191250412016-1243013924<NA><NA><NA><NA>
98R00000992017-0440164925<NA><NA>2017-04430139242019-0540239218<NA><NA><NA><NA>
99R0000100<NA><NA><NA><NA>2016-12191250412017-07191250412017-0135781503<NA><NA>