Overview

Dataset statistics

Number of variables16
Number of observations156
Missing cells196
Missing cells (%)7.9%
Duplicate rows6
Duplicate rows (%)3.8%
Total size in memory19.6 KiB
Average record size in memory128.8 B

Variable types

Numeric7
Categorical9

Alerts

Dataset has 6 (3.8%) duplicate rowsDuplicates
AdmissionDate has a high cardinality: 139 distinct valuesHigh cardinality
Age has 5 (3.2%) missing valuesMissing
Sex has 7 (4.5%) missing valuesMissing
HeartRate has 3 (1.9%) missing valuesMissing
BPSystolic has 2 (1.3%) missing valuesMissing
BPDiastolic has 7 (4.5%) missing valuesMissing
Oxygen Saturation has 6 (3.8%) missing valuesMissing
AdmissionDate has 4 (2.6%) missing valuesMissing
BloodType has 36 (23.1%) missing valuesMissing
MedicalCondition has 20 (12.8%) missing valuesMissing
Height_m has 4 (2.6%) missing valuesMissing
Weight_kg has 2 (1.3%) missing valuesMissing
Medication has 34 (21.8%) missing valuesMissing
PatientNotes has 31 (19.9%) missing valuesMissing
AdditionalComments has 35 (22.4%) missing valuesMissing
AdmissionDate is uniformly distributedUniform

Reproduction

Analysis started2025-10-11 14:15:25.611963
Analysis finished2025-10-11 14:15:30.642900
Duration5.03 seconds
Software versionydata-profiling vv4.1.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct65
Distinct (%)43.0%
Missing5
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean53.715232
Minimum-1
Maximum90
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)1.9%
Memory size1.3 KiB
2025-10-11T16:15:30.690011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile22
Q136.5
median54
Q371.5
95-th percentile84.5
Maximum90
Range91
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.405101
Coefficient of variation (CV)0.39849221
Kurtosis-0.74829336
Mean53.715232
Median Absolute Deviation (MAD)18
Skewness-0.22044632
Sum8111
Variance458.17837
MonotonicityNot monotonic
2025-10-11T16:15:30.771387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 7
 
4.5%
31 6
 
3.8%
66 5
 
3.2%
51 5
 
3.2%
40 4
 
2.6%
39 4
 
2.6%
52 4
 
2.6%
80 4
 
2.6%
82 4
 
2.6%
90 3
 
1.9%
Other values (55) 105
67.3%
(Missing) 5
 
3.2%
ValueCountFrequency (%)
-1 3
1.9%
18 2
1.3%
21 2
1.3%
22 2
1.3%
23 2
1.3%
24 1
 
0.6%
25 2
1.3%
27 1
 
0.6%
28 1
 
0.6%
29 1
 
0.6%
ValueCountFrequency (%)
90 3
1.9%
89 1
 
0.6%
87 2
1.3%
85 2
1.3%
84 3
1.9%
82 4
2.6%
81 3
1.9%
80 4
2.6%
79 1
 
0.6%
78 3
1.9%

Sex
Categorical

Distinct8
Distinct (%)5.4%
Missing7
Missing (%)4.5%
Memory size1.3 KiB
f
29 
M
27 
Female
21 
male
17 
Male
16 
Other values (3)
39 

Length

Max length6
Median length1
Mean length2.8389262
Min length1

Characters and Unicode

Total characters423
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowfemale
3rd rowf
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
f 29
18.6%
M 27
17.3%
Female 21
13.5%
male 17
10.9%
Male 16
10.3%
female 14
9.0%
F 13
8.3%
m 12
7.7%
(Missing) 7
 
4.5%

Length

2025-10-11T16:15:30.837876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T16:15:30.929952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
f 42
28.2%
m 39
26.2%
female 35
23.5%
male 33
22.1%

Most occurring characters

ValueCountFrequency (%)
e 103
24.3%
a 68
16.1%
l 68
16.1%
m 64
15.1%
f 43
10.2%
M 43
10.2%
F 34
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 346
81.8%
Uppercase Letter 77
 
18.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 103
29.8%
a 68
19.7%
l 68
19.7%
m 64
18.5%
f 43
12.4%
Uppercase Letter
ValueCountFrequency (%)
M 43
55.8%
F 34
44.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 423
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 103
24.3%
a 68
16.1%
l 68
16.1%
m 64
15.1%
f 43
10.2%
M 43
10.2%
F 34
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 103
24.3%
a 68
16.1%
l 68
16.1%
m 64
15.1%
f 43
10.2%
M 43
10.2%
F 34
 
8.0%

HeartRate
Real number (ℝ)

Distinct57
Distinct (%)37.3%
Missing3
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean82.235294
Minimum35
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2025-10-11T16:15:31.016281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile51
Q166
median84
Q398
95-th percentile108.4
Maximum190
Range155
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.34114
Coefficient of variation (CV)0.25951315
Kurtosis3.0500654
Mean82.235294
Median Absolute Deviation (MAD)16
Skewness0.47880125
Sum12582
Variance455.44427
MonotonicityNot monotonic
2025-10-11T16:15:31.095632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 8
 
5.1%
66 7
 
4.5%
106 7
 
4.5%
97 5
 
3.2%
109 5
 
3.2%
85 5
 
3.2%
70 4
 
2.6%
72 4
 
2.6%
101 4
 
2.6%
61 4
 
2.6%
Other values (47) 100
64.1%
ValueCountFrequency (%)
35 3
1.9%
38 3
1.9%
50 1
 
0.6%
51 2
1.3%
52 3
1.9%
53 1
 
0.6%
55 1
 
0.6%
56 1
 
0.6%
57 1
 
0.6%
58 3
1.9%
ValueCountFrequency (%)
190 1
 
0.6%
110 2
 
1.3%
109 5
3.2%
108 3
1.9%
107 3
1.9%
106 7
4.5%
105 1
 
0.6%
104 1
 
0.6%
103 3
1.9%
102 2
 
1.3%

BPSystolic
Real number (ℝ)

Distinct50
Distinct (%)32.5%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean114.06494
Minimum71
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2025-10-11T16:15:31.182608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile85.8
Q1100.25
median114.5
Q3127.75
95-th percentile138
Maximum194
Range123
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation19.302773
Coefficient of variation (CV)0.16922618
Kurtosis2.5969775
Mean114.06494
Median Absolute Deviation (MAD)13.5
Skewness0.51103581
Sum17566
Variance372.59706
MonotonicityNot monotonic
2025-10-11T16:15:31.266999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 9
 
5.8%
131 8
 
5.1%
101 7
 
4.5%
93 6
 
3.8%
98 5
 
3.2%
135 5
 
3.2%
115 5
 
3.2%
107 5
 
3.2%
126 5
 
3.2%
123 4
 
2.6%
Other values (40) 95
60.9%
ValueCountFrequency (%)
71 3
1.9%
73 3
1.9%
78 2
 
1.3%
90 2
 
1.3%
91 2
 
1.3%
92 3
1.9%
93 6
3.8%
94 2
 
1.3%
96 3
1.9%
97 3
1.9%
ValueCountFrequency (%)
194 2
 
1.3%
140 2
 
1.3%
139 3
 
1.9%
138 2
 
1.3%
137 1
 
0.6%
136 2
 
1.3%
135 5
3.2%
134 4
2.6%
133 3
 
1.9%
131 8
5.1%

BPDiastolic
Real number (ℝ)

Distinct31
Distinct (%)20.8%
Missing7
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean74.657718
Minimum60
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2025-10-11T16:15:31.344193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile61
Q167
median73
Q383
95-th percentile89
Maximum90
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.2215756
Coefficient of variation (CV)0.12351805
Kurtosis-1.2696664
Mean74.657718
Median Absolute Deviation (MAD)9
Skewness-0.026861095
Sum11124
Variance85.037457
MonotonicityNot monotonic
2025-10-11T16:15:31.411470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
69 11
 
7.1%
61 10
 
6.4%
82 8
 
5.1%
73 7
 
4.5%
71 7
 
4.5%
78 7
 
4.5%
86 7
 
4.5%
84 6
 
3.8%
62 6
 
3.8%
81 6
 
3.8%
Other values (21) 74
47.4%
(Missing) 7
 
4.5%
ValueCountFrequency (%)
60 6
3.8%
61 10
6.4%
62 6
3.8%
63 2
 
1.3%
64 5
3.2%
65 3
 
1.9%
66 1
 
0.6%
67 5
3.2%
68 4
 
2.6%
69 11
7.1%
ValueCountFrequency (%)
90 4
2.6%
89 5
3.2%
88 4
2.6%
87 2
 
1.3%
86 7
4.5%
85 5
3.2%
84 6
3.8%
83 6
3.8%
82 8
5.1%
81 6
3.8%

Oxygen Saturation
Real number (ℝ)

Distinct14
Distinct (%)9.3%
Missing6
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean94.893333
Minimum75
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2025-10-11T16:15:31.476724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile90
Q192
median95
Q398
95-th percentile100
Maximum102
Range27
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2884998
Coefficient of variation (CV)0.045192846
Kurtosis5.885168
Mean94.893333
Median Absolute Deviation (MAD)3
Skewness-1.7378891
Sum14234
Variance18.39123
MonotonicityNot monotonic
2025-10-11T16:15:31.543817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
97 20
12.8%
95 18
11.5%
92 17
10.9%
99 14
9.0%
96 13
8.3%
100 11
7.1%
93 10
6.4%
90 10
6.4%
98 10
6.4%
94 10
6.4%
Other values (4) 17
10.9%
ValueCountFrequency (%)
75 1
 
0.6%
78 3
 
1.9%
90 10
6.4%
91 9
5.8%
92 17
10.9%
93 10
6.4%
94 10
6.4%
95 18
11.5%
96 13
8.3%
97 20
12.8%
ValueCountFrequency (%)
102 4
 
2.6%
100 11
7.1%
99 14
9.0%
98 10
6.4%
97 20
12.8%
96 13
8.3%
95 18
11.5%
94 10
6.4%
93 10
6.4%
92 17
10.9%

AdmissionDate
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct139
Distinct (%)91.4%
Missing4
Missing (%)2.6%
Memory size1.3 KiB
09-25-2025
 
3
2025-03-03
 
2
2024-10-20
 
2
08-14-2025
 
2
16/01/2025
 
2
Other values (134)
141 

Length

Max length12
Median length10
Mean length10.447368
Min length10

Characters and Unicode

Total characters1588
Distinct characters34
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)83.6%

Sample

1st rowSep 28, 2025
2nd row16/05/2025
3rd rowJan 29, 2025
4th row08-14-2025
5th row07/05/2025

Common Values

ValueCountFrequency (%)
09-25-2025 3
 
1.9%
2025-03-03 2
 
1.3%
2024-10-20 2
 
1.3%
08-14-2025 2
 
1.3%
16/01/2025 2
 
1.3%
01-05-2025 2
 
1.3%
06/07/2025 2
 
1.3%
08-21-2025 2
 
1.3%
06-18-2025 2
 
1.3%
2025-02-07 2
 
1.3%
Other values (129) 131
84.0%
(Missing) 4
 
2.6%

Length

2025-10-11T16:15:31.618157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2025 24
 
10.9%
2024 10
 
4.5%
sep 7
 
3.2%
dec 4
 
1.8%
may 4
 
1.8%
nov 4
 
1.8%
mar 3
 
1.4%
06 3
 
1.4%
28 3
 
1.4%
09-25-2025 3
 
1.4%
Other values (131) 155
70.5%

Most occurring characters

ValueCountFrequency (%)
2 384
24.2%
0 312
19.6%
- 152
 
9.6%
5 136
 
8.6%
1 117
 
7.4%
/ 84
 
5.3%
68
 
4.3%
4 60
 
3.8%
6 35
 
2.2%
3 34
 
2.1%
Other values (24) 206
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1148
72.3%
Dash Punctuation 152
 
9.6%
Other Punctuation 118
 
7.4%
Space Separator 68
 
4.3%
Lowercase Letter 68
 
4.3%
Uppercase Letter 34
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11
16.2%
a 10
14.7%
p 9
13.2%
c 7
10.3%
r 5
7.4%
y 4
 
5.9%
v 4
 
5.9%
o 4
 
5.9%
n 4
 
5.9%
u 4
 
5.9%
Other values (3) 6
8.8%
Decimal Number
ValueCountFrequency (%)
2 384
33.4%
0 312
27.2%
5 136
 
11.8%
1 117
 
10.2%
4 60
 
5.2%
6 35
 
3.0%
3 34
 
3.0%
8 25
 
2.2%
7 23
 
2.0%
9 22
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
S 7
20.6%
M 7
20.6%
J 5
14.7%
A 4
11.8%
D 4
11.8%
N 4
11.8%
O 3
8.8%
Other Punctuation
ValueCountFrequency (%)
/ 84
71.2%
, 34
28.8%
Dash Punctuation
ValueCountFrequency (%)
- 152
100.0%
Space Separator
ValueCountFrequency (%)
68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1486
93.6%
Latin 102
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11
 
10.8%
a 10
 
9.8%
p 9
 
8.8%
S 7
 
6.9%
c 7
 
6.9%
M 7
 
6.9%
r 5
 
4.9%
J 5
 
4.9%
y 4
 
3.9%
v 4
 
3.9%
Other values (10) 33
32.4%
Common
ValueCountFrequency (%)
2 384
25.8%
0 312
21.0%
- 152
 
10.2%
5 136
 
9.2%
1 117
 
7.9%
/ 84
 
5.7%
68
 
4.6%
4 60
 
4.0%
6 35
 
2.4%
3 34
 
2.3%
Other values (4) 104
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 384
24.2%
0 312
19.6%
- 152
 
9.6%
5 136
 
8.6%
1 117
 
7.4%
/ 84
 
5.3%
68
 
4.3%
4 60
 
3.8%
6 35
 
2.2%
3 34
 
2.1%
Other values (24) 206
13.0%

BloodType
Categorical

Distinct12
Distinct (%)10.0%
Missing36
Missing (%)23.1%
Memory size1.3 KiB
AB+
14 
0+
14 
O+
13 
A-
10 
0-
10 
Other values (7)
59 

Length

Max length3
Median length2
Mean length2.1833333
Min length2

Characters and Unicode

Total characters262
Distinct characters8
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb+
2nd rowO+
3rd rowAB-
4th rowA-
5th rowA+

Common Values

ValueCountFrequency (%)
AB+ 14
 
9.0%
0+ 14
 
9.0%
O+ 13
 
8.3%
A- 10
 
6.4%
0- 10
 
6.4%
O- 10
 
6.4%
b+ 9
 
5.8%
A+ 9
 
5.8%
B- 9
 
5.8%
AB- 8
 
5.1%
Other values (2) 14
 
9.0%
(Missing) 36
23.1%

Length

2025-10-11T16:15:31.692480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 26
21.7%
b 25
20.8%
0 24
20.0%
o 23
19.2%
ab 22
18.3%

Most occurring characters

ValueCountFrequency (%)
+ 73
27.9%
- 47
17.9%
A 41
15.6%
B 38
14.5%
0 24
 
9.2%
O 23
 
8.8%
b 9
 
3.4%
a 7
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 102
38.9%
Math Symbol 73
27.9%
Dash Punctuation 47
17.9%
Decimal Number 24
 
9.2%
Lowercase Letter 16
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 41
40.2%
B 38
37.3%
O 23
22.5%
Lowercase Letter
ValueCountFrequency (%)
b 9
56.2%
a 7
43.8%
Math Symbol
ValueCountFrequency (%)
+ 73
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%
Decimal Number
ValueCountFrequency (%)
0 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 144
55.0%
Latin 118
45.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 41
34.7%
B 38
32.2%
O 23
19.5%
b 9
 
7.6%
a 7
 
5.9%
Common
ValueCountFrequency (%)
+ 73
50.7%
- 47
32.6%
0 24
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 73
27.9%
- 47
17.9%
A 41
15.6%
B 38
14.5%
0 24
 
9.2%
O 23
 
8.8%
b 9
 
3.4%
a 7
 
2.7%

MedicalCondition
Categorical

Distinct9
Distinct (%)6.6%
Missing20
Missing (%)12.8%
Memory size1.3 KiB
Asthma
30 
HYPERTENSION
24 
diabetes
23 
Diabetes
22 
Hypertension
18 
Other values (4)
19 

Length

Max length15
Median length13
Mean length9.375
Min length6

Characters and Unicode

Total characters1275
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiabetes
2nd rowHYPERTENSION
3rd rowHYPERTENSION
4th rowFabry Disease
5th rowDiabetes

Common Values

ValueCountFrequency (%)
Asthma 30
19.2%
HYPERTENSION 24
15.4%
diabetes 23
14.7%
Diabetes 22
14.1%
Hypertension 18
11.5%
Hemochromatosis 6
 
3.8%
Amyloidosis 6
 
3.8%
Porphyria 4
 
2.6%
Fabry Disease 3
 
1.9%
(Missing) 20
12.8%

Length

2025-10-11T16:15:31.759838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T16:15:31.844429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
diabetes 45
32.4%
hypertension 42
30.2%
asthma 30
21.6%
hemochromatosis 6
 
4.3%
amyloidosis 6
 
4.3%
porphyria 4
 
2.9%
fabry 3
 
2.2%
disease 3
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e 138
 
10.8%
s 123
 
9.6%
t 99
 
7.8%
a 91
 
7.1%
i 88
 
6.9%
o 52
 
4.1%
E 48
 
3.8%
N 48
 
3.8%
b 48
 
3.8%
H 48
 
3.8%
Other values (20) 492
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 892
70.0%
Uppercase Letter 380
29.8%
Space Separator 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 138
15.5%
s 123
13.8%
t 99
11.1%
a 91
10.2%
i 88
9.9%
o 52
 
5.8%
b 48
 
5.4%
m 48
 
5.4%
h 40
 
4.5%
n 36
 
4.0%
Other values (6) 129
14.5%
Uppercase Letter
ValueCountFrequency (%)
E 48
12.6%
N 48
12.6%
H 48
12.6%
A 36
9.5%
P 28
7.4%
D 25
6.6%
O 24
6.3%
I 24
6.3%
S 24
6.3%
T 24
6.3%
Other values (3) 51
13.4%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1272
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 138
 
10.8%
s 123
 
9.7%
t 99
 
7.8%
a 91
 
7.2%
i 88
 
6.9%
o 52
 
4.1%
E 48
 
3.8%
N 48
 
3.8%
b 48
 
3.8%
H 48
 
3.8%
Other values (19) 489
38.4%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 138
 
10.8%
s 123
 
9.6%
t 99
 
7.8%
a 91
 
7.1%
i 88
 
6.9%
o 52
 
4.1%
E 48
 
3.8%
N 48
 
3.8%
b 48
 
3.8%
H 48
 
3.8%
Other values (20) 492
38.6%

AttendingPhysician
Categorical

Distinct7
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Johnson
29 
Dr. Williams
28 
dr. johnson
25 
Dr. Brown
23 
Dr. Smith
20 
Other values (2)
31 

Length

Max length12
Median length11
Mean length9.9807692
Min length7

Characters and Unicode

Total characters1557
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohnson
2nd rowJohnson
3rd rowdr. johnson
4th rowDr. Johnson
5th rowJohnson

Common Values

ValueCountFrequency (%)
Johnson 29
18.6%
Dr. Williams 28
17.9%
dr. johnson 25
16.0%
Dr. Brown 23
14.7%
Dr. Smith 20
12.8%
Dr. Johnson 16
10.3%
DR. WILLIAMS 15
9.6%

Length

2025-10-11T16:15:31.934276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T16:15:32.014426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
dr 127
44.9%
johnson 70
24.7%
williams 43
 
15.2%
brown 23
 
8.1%
smith 20
 
7.1%

Most occurring characters

ValueCountFrequency (%)
n 163
 
10.5%
o 163
 
10.5%
r 135
 
8.7%
. 127
 
8.2%
127
 
8.2%
D 102
 
6.6%
s 98
 
6.3%
h 90
 
5.8%
i 76
 
4.9%
l 56
 
3.6%
Other values (15) 420
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 950
61.0%
Uppercase Letter 353
 
22.7%
Other Punctuation 127
 
8.2%
Space Separator 127
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 163
17.2%
o 163
17.2%
r 135
14.2%
s 98
10.3%
h 90
9.5%
i 76
8.0%
l 56
 
5.9%
m 48
 
5.1%
a 28
 
2.9%
j 25
 
2.6%
Other values (3) 68
7.2%
Uppercase Letter
ValueCountFrequency (%)
D 102
28.9%
J 45
12.7%
W 43
12.2%
S 35
 
9.9%
L 30
 
8.5%
I 30
 
8.5%
B 23
 
6.5%
R 15
 
4.2%
A 15
 
4.2%
M 15
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 127
100.0%
Space Separator
ValueCountFrequency (%)
127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1303
83.7%
Common 254
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 163
12.5%
o 163
12.5%
r 135
10.4%
D 102
 
7.8%
s 98
 
7.5%
h 90
 
6.9%
i 76
 
5.8%
l 56
 
4.3%
m 48
 
3.7%
J 45
 
3.5%
Other values (13) 327
25.1%
Common
ValueCountFrequency (%)
. 127
50.0%
127
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 163
 
10.5%
o 163
 
10.5%
r 135
 
8.7%
. 127
 
8.2%
127
 
8.2%
D 102
 
6.6%
s 98
 
6.3%
h 90
 
5.8%
i 76
 
4.9%
l 56
 
3.6%
Other values (15) 420
27.0%

AssignedNurse
Categorical

Distinct4
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Nurse Baker
42 
Nurse Clark
42 
Nurse Adams
41 
N. Davis
31 

Length

Max length11
Median length11
Mean length10.403846
Min length8

Characters and Unicode

Total characters1623
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNurse Baker
2nd rowNurse Clark
3rd rowNurse Clark
4th rowN. Davis
5th rowNurse Adams

Common Values

ValueCountFrequency (%)
Nurse Baker 42
26.9%
Nurse Clark 42
26.9%
Nurse Adams 41
26.3%
N. Davis 31
19.9%

Length

2025-10-11T16:15:32.188941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T16:15:32.264634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
nurse 125
40.1%
baker 42
 
13.5%
clark 42
 
13.5%
adams 41
 
13.1%
n 31
 
9.9%
davis 31
 
9.9%

Most occurring characters

ValueCountFrequency (%)
r 209
12.9%
s 197
12.1%
e 167
10.3%
N 156
9.6%
156
9.6%
a 156
9.6%
u 125
7.7%
k 84
 
5.2%
l 42
 
2.6%
C 42
 
2.6%
Other values (8) 289
17.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1124
69.3%
Uppercase Letter 312
 
19.2%
Space Separator 156
 
9.6%
Other Punctuation 31
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 209
18.6%
s 197
17.5%
e 167
14.9%
a 156
13.9%
u 125
11.1%
k 84
7.5%
l 42
 
3.7%
d 41
 
3.6%
m 41
 
3.6%
v 31
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
N 156
50.0%
C 42
 
13.5%
B 42
 
13.5%
A 41
 
13.1%
D 31
 
9.9%
Space Separator
ValueCountFrequency (%)
156
100.0%
Other Punctuation
ValueCountFrequency (%)
. 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1436
88.5%
Common 187
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 209
14.6%
s 197
13.7%
e 167
11.6%
N 156
10.9%
a 156
10.9%
u 125
8.7%
k 84
 
5.8%
l 42
 
2.9%
C 42
 
2.9%
B 42
 
2.9%
Other values (6) 216
15.0%
Common
ValueCountFrequency (%)
156
83.4%
. 31
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1623
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 209
12.9%
s 197
12.1%
e 167
10.3%
N 156
9.6%
156
9.6%
a 156
9.6%
u 125
7.7%
k 84
 
5.2%
l 42
 
2.6%
C 42
 
2.6%
Other values (8) 289
17.8%

Height_m
Real number (ℝ)

Distinct78
Distinct (%)51.3%
Missing4
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean2.7057895
Minimum1.51
Maximum6.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2025-10-11T16:15:32.335826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.51
5-th percentile1.56
Q11.69
median1.82
Q31.9925
95-th percentile6.1545
Maximum6.5
Range4.99
Interquartile range (IQR)0.3025

Descriptive statistics

Standard deviation1.7221269
Coefficient of variation (CV)0.63646007
Kurtosis-0.24950948
Mean2.7057895
Median Absolute Deviation (MAD)0.165
Skewness1.2845702
Sum411.28
Variance2.9657212
MonotonicityNot monotonic
2025-10-11T16:15:32.415157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.77 6
 
3.8%
1.99 6
 
3.8%
1.88 5
 
3.2%
1.65 5
 
3.2%
1.74 4
 
2.6%
1.72 4
 
2.6%
1.56 4
 
2.6%
1.59 4
 
2.6%
1.78 4
 
2.6%
1.79 4
 
2.6%
Other values (68) 106
67.9%
(Missing) 4
 
2.6%
ValueCountFrequency (%)
1.51 1
 
0.6%
1.52 1
 
0.6%
1.53 2
1.3%
1.54 1
 
0.6%
1.55 1
 
0.6%
1.56 4
2.6%
1.57 3
1.9%
1.58 1
 
0.6%
1.59 4
2.6%
1.6 2
1.3%
ValueCountFrequency (%)
6.5 2
1.3%
6.33 1
0.6%
6.28 2
1.3%
6.27 1
0.6%
6.26 1
0.6%
6.16 1
0.6%
6.15 1
0.6%
6.06 1
0.6%
6.05 1
0.6%
6.02 1
0.6%

Weight_kg
Real number (ℝ)

Distinct78
Distinct (%)50.6%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean102.15584
Minimum50
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2025-10-11T16:15:32.494915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile57
Q175
median93.5
Q3112.75
95-th percentile202
Maximum220
Range170
Interquartile range (IQR)37.75

Descriptive statistics

Standard deviation42.13519
Coefficient of variation (CV)0.41245991
Kurtosis1.2946209
Mean102.15584
Median Absolute Deviation (MAD)19
Skewness1.4113018
Sum15732
Variance1775.3742
MonotonicityNot monotonic
2025-10-11T16:15:32.574068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 5
 
3.2%
61 5
 
3.2%
117 5
 
3.2%
101 4
 
2.6%
103 4
 
2.6%
107 3
 
1.9%
112 3
 
1.9%
84 3
 
1.9%
75 3
 
1.9%
97 3
 
1.9%
Other values (68) 116
74.4%
ValueCountFrequency (%)
50 1
 
0.6%
52 3
1.9%
53 1
 
0.6%
55 1
 
0.6%
56 1
 
0.6%
57 2
 
1.3%
58 2
 
1.3%
59 3
1.9%
61 5
3.2%
62 1
 
0.6%
ValueCountFrequency (%)
220 1
 
0.6%
217 1
 
0.6%
215 2
1.3%
208 1
 
0.6%
206 1
 
0.6%
203 1
 
0.6%
202 2
1.3%
201 3
1.9%
198 1
 
0.6%
190 1
 
0.6%

Medication
Categorical

Distinct7
Distinct (%)5.7%
Missing34
Missing (%)21.8%
Memory size1.3 KiB
Ibuprofen
38 
Aspirin
34 
Paracetamol
32 
Nusinersen
5 
Inotersen
5 
Other values (2)
8 

Length

Max length11
Median length10
Mean length9.0409836
Min length7

Characters and Unicode

Total characters1103
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAspirin
2nd rowIbuprofen
3rd rowIbuprofen
4th rowIbuprofen
5th rowIbuprofen

Common Values

ValueCountFrequency (%)
Ibuprofen 38
24.4%
Aspirin 34
21.8%
Paracetamol 32
20.5%
Nusinersen 5
 
3.2%
Inotersen 5
 
3.2%
Eculizumab 4
 
2.6%
Patisiran 4
 
2.6%
(Missing) 34
21.8%

Length

2025-10-11T16:15:32.651888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T16:15:32.731654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ibuprofen 38
31.1%
aspirin 34
27.9%
paracetamol 32
26.2%
nusinersen 5
 
4.1%
inotersen 5
 
4.1%
eculizumab 4
 
3.3%
patisiran 4
 
3.3%

Most occurring characters

ValueCountFrequency (%)
r 118
 
10.7%
a 108
 
9.8%
n 96
 
8.7%
e 90
 
8.2%
i 85
 
7.7%
o 75
 
6.8%
p 72
 
6.5%
s 53
 
4.8%
u 51
 
4.6%
I 43
 
3.9%
Other values (11) 312
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 981
88.9%
Uppercase Letter 122
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 118
12.0%
a 108
11.0%
n 96
9.8%
e 90
9.2%
i 85
8.7%
o 75
 
7.6%
p 72
 
7.3%
s 53
 
5.4%
u 51
 
5.2%
b 42
 
4.3%
Other values (6) 191
19.5%
Uppercase Letter
ValueCountFrequency (%)
I 43
35.2%
P 36
29.5%
A 34
27.9%
N 5
 
4.1%
E 4
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1103
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 118
 
10.7%
a 108
 
9.8%
n 96
 
8.7%
e 90
 
8.2%
i 85
 
7.7%
o 75
 
6.8%
p 72
 
6.5%
s 53
 
4.8%
u 51
 
4.6%
I 43
 
3.9%
Other values (11) 312
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 118
 
10.7%
a 108
 
9.8%
n 96
 
8.7%
e 90
 
8.2%
i 85
 
7.7%
o 75
 
6.8%
p 72
 
6.5%
s 53
 
4.8%
u 51
 
4.6%
I 43
 
3.9%
Other values (11) 312
28.3%

PatientNotes
Categorical

Distinct5
Distinct (%)4.0%
Missing31
Missing (%)19.9%
Memory size1.3 KiB
No complaints
32 
Patient complained of dizziness
26 
Patient stable
25 
Needs follow-up
24 
Patient reported feeling better
18 

Length

Max length31
Median length15
Mean length19.92
Min length13

Characters and Unicode

Total characters2490
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo complaints
2nd rowPatient complained of dizziness
3rd rowPatient complained of dizziness
4th rowPatient complained of dizziness
5th rowNeeds follow-up

Common Values

ValueCountFrequency (%)
No complaints 32
20.5%
Patient complained of dizziness 26
16.7%
Patient stable 25
16.0%
Needs follow-up 24
15.4%
Patient reported feeling better 18
11.5%
(Missing) 31
19.9%

Length

2025-10-11T16:15:32.802209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T16:15:32.880126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
patient 69
20.4%
no 32
9.5%
complaints 32
9.5%
complained 26
 
7.7%
of 26
 
7.7%
dizziness 26
 
7.7%
stable 25
 
7.4%
needs 24
 
7.1%
follow-up 24
 
7.1%
reported 18
 
5.3%
Other values (2) 36
10.7%

Most occurring characters

ValueCountFrequency (%)
e 302
12.1%
t 249
 
10.0%
213
 
8.6%
i 197
 
7.9%
o 182
 
7.3%
n 171
 
6.9%
a 152
 
6.1%
l 149
 
6.0%
s 133
 
5.3%
p 100
 
4.0%
Other values (13) 642
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2128
85.5%
Space Separator 213
 
8.6%
Uppercase Letter 125
 
5.0%
Dash Punctuation 24
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 302
14.2%
t 249
11.7%
i 197
9.3%
o 182
8.6%
n 171
8.0%
a 152
 
7.1%
l 149
 
7.0%
s 133
 
6.2%
p 100
 
4.7%
d 94
 
4.4%
Other values (9) 399
18.8%
Uppercase Letter
ValueCountFrequency (%)
P 69
55.2%
N 56
44.8%
Space Separator
ValueCountFrequency (%)
213
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2253
90.5%
Common 237
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 302
13.4%
t 249
11.1%
i 197
 
8.7%
o 182
 
8.1%
n 171
 
7.6%
a 152
 
6.7%
l 149
 
6.6%
s 133
 
5.9%
p 100
 
4.4%
d 94
 
4.2%
Other values (11) 524
23.3%
Common
ValueCountFrequency (%)
213
89.9%
- 24
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 302
12.1%
t 249
 
10.0%
213
 
8.6%
i 197
 
7.9%
o 182
 
7.3%
n 171
 
6.9%
a 152
 
6.1%
l 149
 
6.0%
s 133
 
5.3%
p 100
 
4.0%
Other values (13) 642
25.8%

AdditionalComments
Categorical

Distinct4
Distinct (%)3.3%
Missing35
Missing (%)22.4%
Memory size1.3 KiB
Non-smoker
40 
Regular exercise routine
28 
Family history of heart disease
27 
Allergic to penicillin
26 

Length

Max length31
Median length24
Mean length20.504132
Min length10

Characters and Unicode

Total characters2481
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFamily history of heart disease
2nd rowAllergic to penicillin
3rd rowRegular exercise routine
4th rowFamily history of heart disease
5th rowRegular exercise routine

Common Values

ValueCountFrequency (%)
Non-smoker 40
25.6%
Regular exercise routine 28
17.9%
Family history of heart disease 27
17.3%
Allergic to penicillin 26
16.7%
(Missing) 35
22.4%

Length

2025-10-11T16:15:32.947369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-11T16:15:33.016118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
non-smoker 40
11.9%
regular 28
8.3%
exercise 28
8.3%
routine 28
8.3%
family 27
8.0%
history 27
8.0%
of 27
8.0%
heart 27
8.0%
disease 27
8.0%
allergic 26
7.7%
Other values (2) 52
15.4%

Most occurring characters

ValueCountFrequency (%)
e 313
12.6%
i 241
 
9.7%
216
 
8.7%
r 204
 
8.2%
o 188
 
7.6%
l 159
 
6.4%
s 149
 
6.0%
n 120
 
4.8%
a 109
 
4.4%
t 108
 
4.4%
Other values (16) 674
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2104
84.8%
Space Separator 216
 
8.7%
Uppercase Letter 121
 
4.9%
Dash Punctuation 40
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 313
14.9%
i 241
11.5%
r 204
9.7%
o 188
8.9%
l 159
 
7.6%
s 149
 
7.1%
n 120
 
5.7%
a 109
 
5.2%
t 108
 
5.1%
c 80
 
3.8%
Other values (10) 433
20.6%
Uppercase Letter
ValueCountFrequency (%)
N 40
33.1%
R 28
23.1%
F 27
22.3%
A 26
21.5%
Space Separator
ValueCountFrequency (%)
216
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2225
89.7%
Common 256
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 313
14.1%
i 241
10.8%
r 204
 
9.2%
o 188
 
8.4%
l 159
 
7.1%
s 149
 
6.7%
n 120
 
5.4%
a 109
 
4.9%
t 108
 
4.9%
c 80
 
3.6%
Other values (14) 554
24.9%
Common
ValueCountFrequency (%)
216
84.4%
- 40
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2481
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 313
12.6%
i 241
 
9.7%
216
 
8.7%
r 204
 
8.2%
o 188
 
7.6%
l 159
 
6.4%
s 149
 
6.0%
n 120
 
4.8%
a 109
 
4.4%
t 108
 
4.4%
Other values (16) 674
27.2%

Interactions

2025-10-11T16:15:29.644142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:26.738210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.321088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.879442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.351913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.783714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.204156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.700871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:26.810893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.401855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.951945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.410200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.839005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.258584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.772176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:26.891763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.488167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.027867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.474654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.903152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.321408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.841285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:26.967429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.577658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.112324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.538105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.964118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.401033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.908397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.031284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.667025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.172504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.599085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.026079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.464998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.971370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.171214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.728273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.235344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.660740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.083126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.525333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:30.031661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.244048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:27.798305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.291793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:28.718271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.139990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-10-11T16:15:29.580062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2025-10-11T16:15:33.082831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AgeHeartRateBPSystolicBPDiastolicOxygen SaturationHeight_mWeight_kgSexBloodTypeMedicalConditionAttendingPhysicianAssignedNurseMedicationPatientNotesAdditionalComments
Age1.0000.054-0.0580.0390.168-0.027-0.0300.1480.0000.0000.0000.0980.0000.1360.000
HeartRate0.0541.000-0.0570.107-0.151-0.1110.0420.0560.1850.2030.0000.1150.0000.1910.116
BPSystolic-0.058-0.0571.000-0.110-0.0550.102-0.1300.0000.0000.0000.1240.0000.1080.0860.129
BPDiastolic0.0390.107-0.1101.0000.092-0.116-0.0580.0900.1610.0000.0000.1360.0510.0000.099
Oxygen Saturation0.168-0.151-0.0550.0921.0000.144-0.0990.0000.1720.0000.0760.1470.1460.0830.000
Height_m-0.027-0.1110.102-0.1160.1441.000-0.1520.1410.0740.0770.0730.1160.1520.0000.066
Weight_kg-0.0300.042-0.130-0.058-0.099-0.1521.0000.1520.1850.1850.0000.0000.0000.1140.000
Sex0.1480.0560.0000.0900.0000.1410.1521.0000.1290.0470.1090.0530.1550.0000.114
BloodType0.0000.1850.0000.1610.1720.0740.1850.1291.0000.1120.0300.1100.0120.1970.098
MedicalCondition0.0000.2030.0000.0000.0000.0770.1850.0470.1121.0000.0000.1350.0000.1620.000
AttendingPhysician0.0000.0000.1240.0000.0760.0730.0000.1090.0300.0001.0000.0000.0660.1450.016
AssignedNurse0.0980.1150.0000.1360.1470.1160.0000.0530.1100.1350.0001.0000.0000.1480.171
Medication0.0000.0000.1080.0510.1460.1520.0000.1550.0120.0000.0660.0001.0000.1780.160
PatientNotes0.1360.1910.0860.0000.0830.0000.1140.0000.1970.1620.1450.1480.1781.0000.088
AdditionalComments0.0000.1160.1290.0990.0000.0660.0000.1140.0980.0000.0160.1710.1600.0881.000

Missing values

2025-10-11T16:15:30.235628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-11T16:15:30.396219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-11T16:15:30.537708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeSexHeartRateBPSystolicBPDiastolicOxygen SaturationAdmissionDateBloodTypeMedicalConditionAttendingPhysicianAssignedNurseHeight_mWeight_kgMedicationPatientNotesAdditionalComments
021.0Female97.0124.060.093.0Sep 28, 2025b+DiabetesJohnsonNurse Baker1.67208.0AspirinNo complaintsFamily history of heart disease
151.0female109.0130.078.078.016/05/2025O+HYPERTENSIONJohnsonNurse Clark5.5359.0IbuprofenPatient complained of dizzinessAllergic to penicillin
289.0f99.0110.089.095.0Jan 29, 2025NaNHYPERTENSIONdr. johnsonNurse Clark1.7196.0IbuprofenPatient complained of dizzinessRegular exercise routine
3NaNMale77.0128.077.090.008-14-2025AB-NaNDr. JohnsonN. DavisNaN83.0IbuprofenPatient complained of dizzinessFamily history of heart disease
482.0Male60.0123.075.0NaN07/05/2025A-Fabry DiseaseJohnsonNurse Adams1.77202.0IbuprofenNeeds follow-upNaN
545.0Female75.0118.067.078.02024-12-14A+DiabetesJohnsonNurse Clark5.3677.0NaNPatient stableRegular exercise routine
630.0f79.0131.072.091.006/07/2025AB+DiabetesDr. BrownNurse Adams6.5061.0IbuprofenPatient stableRegular exercise routine
769.0Male74.0119.083.098.002-04-2025A+diabetesDr. SmithNurse Clark5.88117.0AspirinPatient stableFamily history of heart disease
848.0Female52.0127.068.095.003-23-2025A-HemochromatosisDr. SmithNurse Baker1.5758.0ParacetamolNeeds follow-upAllergic to penicillin
956.0M85.098.083.094.026/06/2025AB+AsthmaJohnsonNurse Adams5.6450.0IbuprofenNaNNon-smoker
AgeSexHeartRateBPSystolicBPDiastolicOxygen SaturationAdmissionDateBloodTypeMedicalConditionAttendingPhysicianAssignedNurseHeight_mWeight_kgMedicationPatientNotesAdditionalComments
14661.0M35.0120.062.095.0Sep 22, 2025a+diabetesDr. SmithNurse Baker1.72116.0AspirinPatient complained of dizzinessNaN
14739.0f50.0107.072.093.02024-10-160+diabetesDr. WilliamsNurse Clark1.6474.0NaNPatient complained of dizzinessFamily history of heart disease
14831.0m57.0124.082.096.0Jun 09, 2025b+NaNDr. SmithNurse Clark5.9579.0NaNNeeds follow-upNaN
14951.0F97.0101.088.092.027/10/2024B-HypertensionJohnsonNurse Clark1.6095.0AspirinPatient reported feeling betterFamily history of heart disease
15018.0M73.0126.084.094.0Dec 20, 2024a+PorphyriaDr. BrownNurse Baker1.5690.0AspirinNaNAllergic to penicillin
15164.0m66.0129.073.095.020/03/2025b+DiabetesDR. WILLIAMSNurse Clark1.7875.0IbuprofenNo complaintsFamily history of heart disease
15284.0f69.093.061.094.011-23-2024AB-AsthmaDr. WilliamsNurse Adams1.6875.0IbuprofenNeeds follow-upNon-smoker
15375.0male79.0100.073.099.009-05-2025AB-HYPERTENSIONDr. WilliamsN. Davis1.7296.0NaNNo complaintsAllergic to penicillin
154NaNF75.0120.0NaN91.0Nov 29, 2024B-NaNDR. WILLIAMSNurse Baker6.26NaNParacetamolNo complaintsRegular exercise routine
15552.0Male94.0119.078.0100.0May 06, 2025A-AsthmaDr. BrownNurse Clark5.0267.0NaNPatient complained of dizzinessFamily history of heart disease

Duplicate rows

Most frequently occurring

AgeSexHeartRateBPSystolicBPDiastolicOxygen SaturationAdmissionDateBloodTypeMedicalConditionAttendingPhysicianAssignedNurseHeight_mWeight_kgMedicationPatientNotesAdditionalComments# duplicates
030.0Female66.0101.073.099.009-25-2025O-AmyloidosisDr. WilliamsNurse Baker1.7784.0AspirinPatient reported feeling betterAllergic to penicillin2
130.0f79.0131.072.091.006/07/2025AB+DiabetesDr. BrownNurse Adams6.5061.0IbuprofenPatient stableRegular exercise routine2
262.0Male58.093.069.095.006-18-2025AB+diabetesDr. WilliamsNurse Baker1.81201.0IbuprofenPatient stableNaN2
371.0M66.0139.061.092.0Nov 28, 2024NaNDiabetesdr. johnsonNurse Adams5.66102.0NaNPatient complained of dizzinessNaN2
478.0M101.0135.061.097.016/01/2025AB+AsthmaDR. WILLIAMSNurse Adams1.82166.0NaNNeeds follow-upNon-smoker2
590.0M110.0131.079.097.001-05-20250-NaNDr. SmithNurse Adams5.6293.0ParacetamolNaNNon-smoker2