Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations6,680
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory394.3 B

Variable types

Numeric13
Categorical7

Reproduction

Analysis started2025-06-30 08:31:32.185029
Analysis finished2025-06-30 08:32:34.429545
Duration1 minute and 2.24 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct50
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.625749
Minimum20
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:35.006115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q132
median45
Q357
95-th percentile67
Maximum69
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.325345
Coefficient of variation (CV)0.32101075
Kurtosis-1.1830562
Mean44.625749
Median Absolute Deviation (MAD)12
Skewness-0.0039307256
Sum298100
Variance205.21551
MonotonicityNot monotonic
2025-06-30T09:32:35.405082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 163
 
2.4%
66 155
 
2.3%
68 153
 
2.3%
51 153
 
2.3%
54 151
 
2.3%
45 150
 
2.2%
44 149
 
2.2%
40 148
 
2.2%
36 147
 
2.2%
47 146
 
2.2%
Other values (40) 5165
77.3%
ValueCountFrequency (%)
20 121
1.8%
21 126
1.9%
22 133
2.0%
23 134
2.0%
24 127
1.9%
25 131
2.0%
26 119
1.8%
27 137
2.1%
28 129
1.9%
29 130
1.9%
ValueCountFrequency (%)
69 116
1.7%
68 153
2.3%
67 122
1.8%
66 155
2.3%
65 116
1.7%
64 163
2.4%
63 133
2.0%
62 118
1.8%
61 135
2.0%
60 116
1.7%

Sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.4 KiB
Male
3362 
Female
3318 

Length

Max length6
Median length4
Mean length4.9934132
Min length4

Characters and Unicode

Total characters33,356
Distinct characters6
Distinct categories1 ?
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 rowMale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 3362
50.3%
Female 3318
49.7%

Length

2025-06-30T09:32:35.795362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T09:32:36.190368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 3362
50.3%
female 3318
49.7%

Most occurring characters

ValueCountFrequency (%)
e 9998
30.0%
a 6680
20.0%
l 6680
20.0%
M 3362
 
10.1%
F 3318
 
9.9%
m 3318
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9998
30.0%
a 6680
20.0%
l 6680
20.0%
M 3362
 
10.1%
F 3318
 
9.9%
m 3318
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9998
30.0%
a 6680
20.0%
l 6680
20.0%
M 3362
 
10.1%
F 3318
 
9.9%
m 3318
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9998
30.0%
a 6680
20.0%
l 6680
20.0%
M 3362
 
10.1%
F 3318
 
9.9%
m 3318
 
9.9%

Ethnicity
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size409.3 KiB
Black
1707 
White
1671 
Hispanic
1653 
Asian
1649 

Length

Max length8
Median length5
Mean length5.7423653
Min length5

Characters and Unicode

Total characters38,359
Distinct characters15
Distinct categories1 ?
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 rowWhite
2nd rowAsian
3rd rowBlack
4th rowAsian
5th rowAsian

Common Values

ValueCountFrequency (%)
Black 1707
25.6%
White 1671
25.0%
Hispanic 1653
24.7%
Asian 1649
24.7%

Length

2025-06-30T09:32:36.600200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T09:32:36.918717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
black 1707
25.6%
white 1671
25.0%
hispanic 1653
24.7%
asian 1649
24.7%

Most occurring characters

ValueCountFrequency (%)
i 6626
17.3%
a 5009
13.1%
c 3360
8.8%
s 3302
 
8.6%
n 3302
 
8.6%
B 1707
 
4.5%
l 1707
 
4.5%
k 1707
 
4.5%
W 1671
 
4.4%
h 1671
 
4.4%
Other values (5) 8297
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6626
17.3%
a 5009
13.1%
c 3360
8.8%
s 3302
 
8.6%
n 3302
 
8.6%
B 1707
 
4.5%
l 1707
 
4.5%
k 1707
 
4.5%
W 1671
 
4.4%
h 1671
 
4.4%
Other values (5) 8297
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6626
17.3%
a 5009
13.1%
c 3360
8.8%
s 3302
 
8.6%
n 3302
 
8.6%
B 1707
 
4.5%
l 1707
 
4.5%
k 1707
 
4.5%
W 1671
 
4.4%
h 1671
 
4.4%
Other values (5) 8297
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6626
17.3%
a 5009
13.1%
c 3360
8.8%
s 3302
 
8.6%
n 3302
 
8.6%
B 1707
 
4.5%
l 1707
 
4.5%
k 1707
 
4.5%
W 1671
 
4.4%
h 1671
 
4.4%
Other values (5) 8297
21.6%

BMI
Real number (ℝ)

Distinct216
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.549311
Minimum18.5
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:37.295337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.5
5-th percentile19.7
Q124.3
median29.7
Q334.8
95-th percentile39
Maximum40
Range21.5
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.1465145
Coefficient of variation (CV)0.20800872
Kurtosis-1.1685095
Mean29.549311
Median Absolute Deviation (MAD)5.2
Skewness-0.051707265
Sum197389.4
Variance37.77964
MonotonicityNot monotonic
2025-06-30T09:32:37.678838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.6 55
 
0.8%
34.1 46
 
0.7%
39.4 46
 
0.7%
23.7 44
 
0.7%
37.9 43
 
0.6%
33.2 43
 
0.6%
39.3 42
 
0.6%
31.3 42
 
0.6%
32.5 41
 
0.6%
35.3 41
 
0.6%
Other values (206) 6237
93.4%
ValueCountFrequency (%)
18.5 11
 
0.2%
18.6 27
0.4%
18.7 24
0.4%
18.8 23
0.3%
18.9 29
0.4%
19 31
0.5%
19.1 23
0.3%
19.2 26
0.4%
19.3 25
0.4%
19.4 27
0.4%
ValueCountFrequency (%)
40 15
 
0.2%
39.9 38
0.6%
39.8 30
0.4%
39.7 32
0.5%
39.6 35
0.5%
39.5 37
0.6%
39.4 46
0.7%
39.3 42
0.6%
39.2 30
0.4%
39.1 28
0.4%

Waist_Circumference
Real number (ℝ)

Distinct501
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.707305
Minimum70
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:38.132301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile72.495
Q181.9
median94.8
Q3106.8
95-th percentile117.4
Maximum120
Range50
Interquartile range (IQR)24.9

Descriptive statistics

Standard deviation14.40515
Coefficient of variation (CV)0.15210179
Kurtosis-1.1985516
Mean94.707305
Median Absolute Deviation (MAD)12.4
Skewness0.024534075
Sum632644.8
Variance207.50836
MonotonicityNot monotonic
2025-06-30T09:32:38.585468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.5 26
 
0.4%
80.7 26
 
0.4%
73.7 26
 
0.4%
80.9 25
 
0.4%
77.1 23
 
0.3%
78.4 23
 
0.3%
106.8 23
 
0.3%
103 23
 
0.3%
88.9 23
 
0.3%
84.6 23
 
0.3%
Other values (491) 6439
96.4%
ValueCountFrequency (%)
70 4
 
0.1%
70.1 13
0.2%
70.2 7
0.1%
70.3 11
0.2%
70.4 15
0.2%
70.5 16
0.2%
70.6 12
0.2%
70.7 12
0.2%
70.8 11
0.2%
70.9 14
0.2%
ValueCountFrequency (%)
120 5
 
0.1%
119.9 14
0.2%
119.8 9
0.1%
119.7 19
0.3%
119.6 17
0.3%
119.5 17
0.3%
119.4 13
0.2%
119.3 15
0.2%
119.2 10
0.1%
119.1 18
0.3%

Fasting_Blood_Glucose
Real number (ℝ)

Distinct1293
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.67385
Minimum70
Maximum199.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:38.941390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile76.2
Q1101.6
median134.1
Q3167.6
95-th percentile193.8
Maximum199.9
Range129.9
Interquartile range (IQR)66

Descriptive statistics

Standard deviation37.862039
Coefficient of variation (CV)0.28113877
Kurtosis-1.2133324
Mean134.67385
Median Absolute Deviation (MAD)33.1
Skewness0.012587966
Sum899621.3
Variance1433.534
MonotonicityNot monotonic
2025-06-30T09:32:39.395389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190.8 14
 
0.2%
84.5 13
 
0.2%
73.7 12
 
0.2%
131.9 12
 
0.2%
178 12
 
0.2%
128.5 12
 
0.2%
99.9 11
 
0.2%
164.1 11
 
0.2%
196.3 11
 
0.2%
132.1 11
 
0.2%
Other values (1283) 6561
98.2%
ValueCountFrequency (%)
70 2
 
< 0.1%
70.1 4
0.1%
70.2 8
0.1%
70.3 4
0.1%
70.4 4
0.1%
70.5 8
0.1%
70.6 7
0.1%
70.7 5
0.1%
70.8 7
0.1%
70.9 2
 
< 0.1%
ValueCountFrequency (%)
199.9 5
0.1%
199.8 9
0.1%
199.7 6
0.1%
199.6 4
0.1%
199.5 9
0.1%
199.4 6
0.1%
199.3 4
0.1%
199.2 8
0.1%
199.1 5
0.1%
199 5
0.1%

HbA1c
Real number (ℝ)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.515015
Minimum4
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:39.905424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.5
Q16.8
median9.6
Q312.2
95-th percentile14.4
Maximum15
Range11
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation3.1702551
Coefficient of variation (CV)0.33318445
Kurtosis-1.1954627
Mean9.515015
Median Absolute Deviation (MAD)2.7
Skewness-0.013291618
Sum63560.3
Variance10.050517
MonotonicityNot monotonic
2025-06-30T09:32:40.380477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.9 87
 
1.3%
11.7 81
 
1.2%
6.4 78
 
1.2%
11.3 77
 
1.2%
7.2 77
 
1.2%
7.1 75
 
1.1%
11.1 73
 
1.1%
13.3 71
 
1.1%
13.8 71
 
1.1%
14.3 71
 
1.1%
Other values (101) 5919
88.6%
ValueCountFrequency (%)
4 41
0.6%
4.1 66
1.0%
4.2 59
0.9%
4.3 59
0.9%
4.4 60
0.9%
4.5 63
0.9%
4.6 51
0.8%
4.7 59
0.9%
4.8 56
0.8%
4.9 66
1.0%
ValueCountFrequency (%)
15 25
 
0.4%
14.9 70
1.0%
14.8 66
1.0%
14.7 50
0.7%
14.6 61
0.9%
14.5 55
0.8%
14.4 52
0.8%
14.3 71
1.1%
14.2 54
0.8%
14.1 61
0.9%

Blood_Pressure_Systolic
Real number (ℝ)

Distinct90
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.15135
Minimum90
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:40.748599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile94
Q1112
median134
Q3157
95-th percentile175
Maximum179
Range89
Interquartile range (IQR)45

Descriptive statistics

Standard deviation25.952304
Coefficient of variation (CV)0.19345541
Kurtosis-1.2047147
Mean134.15135
Median Absolute Deviation (MAD)22.5
Skewness0.013437745
Sum896131
Variance673.52208
MonotonicityNot monotonic
2025-06-30T09:32:41.125129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158 92
 
1.4%
104 90
 
1.3%
125 90
 
1.3%
148 89
 
1.3%
99 86
 
1.3%
112 86
 
1.3%
116 86
 
1.3%
93 85
 
1.3%
146 84
 
1.3%
130 84
 
1.3%
Other values (80) 5808
86.9%
ValueCountFrequency (%)
90 54
0.8%
91 83
1.2%
92 77
1.2%
93 85
1.3%
94 77
1.2%
95 69
1.0%
96 68
1.0%
97 81
1.2%
98 83
1.2%
99 86
1.3%
ValueCountFrequency (%)
179 74
1.1%
178 75
1.1%
177 66
1.0%
176 74
1.1%
175 69
1.0%
174 79
1.2%
173 75
1.1%
172 62
0.9%
171 73
1.1%
170 63
0.9%

Blood_Pressure_Diastolic
Real number (ℝ)

Distinct60
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.692216
Minimum60
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:41.489555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile62
Q175
median90
Q3105
95-th percentile117
Maximum119
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.309151
Coefficient of variation (CV)0.19298387
Kurtosis-1.1983942
Mean89.692216
Median Absolute Deviation (MAD)15
Skewness-0.01424509
Sum599144
Variance299.60671
MonotonicityNot monotonic
2025-06-30T09:32:41.965402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 137
 
2.1%
89 129
 
1.9%
92 128
 
1.9%
64 127
 
1.9%
107 124
 
1.9%
112 124
 
1.9%
69 122
 
1.8%
71 121
 
1.8%
113 121
 
1.8%
118 120
 
1.8%
Other values (50) 5427
81.2%
ValueCountFrequency (%)
60 117
1.8%
61 106
1.6%
62 116
1.7%
63 96
1.4%
64 127
1.9%
65 108
1.6%
66 93
1.4%
67 97
1.5%
68 108
1.6%
69 122
1.8%
ValueCountFrequency (%)
119 110
1.6%
118 120
1.8%
117 107
1.6%
116 103
1.5%
115 112
1.7%
114 113
1.7%
113 121
1.8%
112 124
1.9%
111 111
1.7%
110 120
1.8%

Cholesterol_Total
Real number (ℝ)

Distinct1482
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.12138
Minimum150
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:42.482042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile157.895
Q1187.9
median225.6
Q3262.6
95-th percentile291.705
Maximum300
Range150
Interquartile range (IQR)74.7

Descriptive statistics

Standard deviation42.947805
Coefficient of variation (CV)0.19077622
Kurtosis-1.195057
Mean225.12138
Median Absolute Deviation (MAD)37.3
Skewness-0.010800493
Sum1503810.8
Variance1844.514
MonotonicityNot monotonic
2025-06-30T09:32:42.997857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280.5 15
 
0.2%
191.5 14
 
0.2%
171.7 12
 
0.2%
245 12
 
0.2%
188.1 12
 
0.2%
157.2 11
 
0.2%
156.6 11
 
0.2%
182.1 11
 
0.2%
262.6 11
 
0.2%
259.3 11
 
0.2%
Other values (1472) 6560
98.2%
ValueCountFrequency (%)
150 4
0.1%
150.1 3
 
< 0.1%
150.2 3
 
< 0.1%
150.3 8
0.1%
150.4 5
0.1%
150.5 6
0.1%
150.6 4
0.1%
150.7 6
0.1%
150.8 4
0.1%
150.9 5
0.1%
ValueCountFrequency (%)
300 4
0.1%
299.9 1
 
< 0.1%
299.8 4
0.1%
299.7 5
0.1%
299.6 5
0.1%
299.5 2
 
< 0.1%
299.4 2
 
< 0.1%
299.3 8
0.1%
299.2 1
 
< 0.1%
299.1 4
0.1%

Cholesterol_HDL
Real number (ℝ)

Distinct501
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.021931
Minimum30
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:43.410156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile32.5
Q142.175
median55.2
Q367.9
95-th percentile77.5
Maximum80
Range50
Interquartile range (IQR)25.725

Descriptive statistics

Standard deviation14.564377
Coefficient of variation (CV)0.2647013
Kurtosis-1.2228032
Mean55.021931
Median Absolute Deviation (MAD)12.9
Skewness-0.0055440223
Sum367546.5
Variance212.12107
MonotonicityNot monotonic
2025-06-30T09:32:43.895394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.9 24
 
0.4%
76.3 24
 
0.4%
64.7 24
 
0.4%
33.1 23
 
0.3%
38.4 22
 
0.3%
32.5 22
 
0.3%
47.3 22
 
0.3%
30.5 22
 
0.3%
55.6 22
 
0.3%
74.8 22
 
0.3%
Other values (491) 6453
96.6%
ValueCountFrequency (%)
30 5
 
0.1%
30.1 17
0.3%
30.2 21
0.3%
30.3 16
0.2%
30.4 11
0.2%
30.5 22
0.3%
30.6 15
0.2%
30.7 13
0.2%
30.8 17
0.3%
30.9 8
 
0.1%
ValueCountFrequency (%)
80 4
 
0.1%
79.9 12
0.2%
79.8 6
 
0.1%
79.7 11
0.2%
79.6 15
0.2%
79.5 19
0.3%
79.4 14
0.2%
79.3 12
0.2%
79.2 12
0.2%
79.1 12
0.2%

Cholesterol_LDL
Real number (ℝ)

Distinct1294
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.20816
Minimum70
Maximum199.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:44.432600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile76.395
Q1101.3
median134.25
Q3166.5
95-th percentile193.5
Maximum199.9
Range129.9
Interquartile range (IQR)65.2

Descriptive statistics

Standard deviation37.596195
Coefficient of variation (CV)0.28013346
Kurtosis-1.2105254
Mean134.20816
Median Absolute Deviation (MAD)32.65
Skewness0.017210165
Sum896510.5
Variance1413.4739
MonotonicityNot monotonic
2025-06-30T09:32:45.108202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.8 15
 
0.2%
104.7 13
 
0.2%
76.2 13
 
0.2%
99.3 12
 
0.2%
176.8 12
 
0.2%
104.1 12
 
0.2%
85.7 12
 
0.2%
111.5 12
 
0.2%
169 12
 
0.2%
76.8 12
 
0.2%
Other values (1284) 6555
98.1%
ValueCountFrequency (%)
70 4
 
0.1%
70.1 3
 
< 0.1%
70.2 6
0.1%
70.3 9
0.1%
70.4 5
0.1%
70.5 10
0.1%
70.6 4
 
0.1%
70.7 5
0.1%
70.8 5
0.1%
70.9 8
0.1%
ValueCountFrequency (%)
199.9 5
0.1%
199.8 9
0.1%
199.7 2
 
< 0.1%
199.6 5
0.1%
199.5 3
 
< 0.1%
199.4 2
 
< 0.1%
199.3 1
 
< 0.1%
199.2 2
 
< 0.1%
199.1 7
0.1%
199 4
0.1%

GGT
Real number (ℝ)

Distinct901
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.186572
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:45.545318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.6
Q132.4
median55.5
Q377.7
95-th percentile95.4
Maximum100
Range90
Interquartile range (IQR)45.3

Descriptive statistics

Standard deviation25.986194
Coefficient of variation (CV)0.47087892
Kurtosis-1.2073629
Mean55.186572
Median Absolute Deviation (MAD)22.7
Skewness-0.0090590281
Sum368646.3
Variance675.28226
MonotonicityNot monotonic
2025-06-30T09:32:45.988212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.5 17
 
0.3%
88.8 17
 
0.3%
86.6 17
 
0.3%
64.1 16
 
0.2%
57.7 15
 
0.2%
26.6 15
 
0.2%
83.3 15
 
0.2%
33.7 15
 
0.2%
65.6 15
 
0.2%
86.1 14
 
0.2%
Other values (891) 6524
97.7%
ValueCountFrequency (%)
10 3
 
< 0.1%
10.1 5
 
0.1%
10.2 5
 
0.1%
10.3 8
0.1%
10.4 7
0.1%
10.5 9
0.1%
10.6 11
0.2%
10.7 5
 
0.1%
10.8 5
 
0.1%
10.9 13
0.2%
ValueCountFrequency (%)
100 5
 
0.1%
99.9 9
0.1%
99.8 7
0.1%
99.7 1
 
< 0.1%
99.6 13
0.2%
99.5 7
0.1%
99.4 6
0.1%
99.3 10
0.1%
99.2 2
 
< 0.1%
99.1 2
 
< 0.1%

Serum_Urate
Real number (ℝ)

Distinct51
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5127695
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:46.463205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.3
Q14.2
median5.5
Q36.8
95-th percentile7.8
Maximum8
Range5
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.4613881
Coefficient of variation (CV)0.26509146
Kurtosis-1.2235795
Mean5.5127695
Median Absolute Deviation (MAD)1.3
Skewness-0.0075323581
Sum36825.3
Variance2.1356552
MonotonicityNot monotonic
2025-06-30T09:32:46.955453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 159
 
2.4%
4.8 155
 
2.3%
3.3 154
 
2.3%
7.7 154
 
2.3%
3.8 153
 
2.3%
7 153
 
2.3%
7.9 150
 
2.2%
7.4 146
 
2.2%
5.6 146
 
2.2%
3.4 146
 
2.2%
Other values (41) 5164
77.3%
ValueCountFrequency (%)
3 63
0.9%
3.1 132
2.0%
3.2 138
2.1%
3.3 154
2.3%
3.4 146
2.2%
3.5 132
2.0%
3.6 121
1.8%
3.7 131
2.0%
3.8 153
2.3%
3.9 137
2.1%
ValueCountFrequency (%)
8 67
1.0%
7.9 150
2.2%
7.8 128
1.9%
7.7 154
2.3%
7.6 135
2.0%
7.5 143
2.1%
7.4 146
2.2%
7.3 133
2.0%
7.2 159
2.4%
7.1 113
1.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.3 KiB
High
2248 
Low
2235 
Moderate
2197 

Length

Max length8
Median length4
Mean length4.980988
Min length3

Characters and Unicode

Total characters33,273
Distinct characters13
Distinct categories1 ?
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 rowModerate
2nd rowModerate
3rd rowLow
4th rowLow
5th rowModerate

Common Values

ValueCountFrequency (%)
High 2248
33.7%
Low 2235
33.5%
Moderate 2197
32.9%

Length

2025-06-30T09:32:47.350558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T09:32:47.742498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
high 2248
33.7%
low 2235
33.5%
moderate 2197
32.9%

Most occurring characters

ValueCountFrequency (%)
o 4432
13.3%
e 4394
13.2%
H 2248
 
6.8%
i 2248
 
6.8%
g 2248
 
6.8%
h 2248
 
6.8%
L 2235
 
6.7%
w 2235
 
6.7%
M 2197
 
6.6%
d 2197
 
6.6%
Other values (3) 6591
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4432
13.3%
e 4394
13.2%
H 2248
 
6.8%
i 2248
 
6.8%
g 2248
 
6.8%
h 2248
 
6.8%
L 2235
 
6.7%
w 2235
 
6.7%
M 2197
 
6.6%
d 2197
 
6.6%
Other values (3) 6591
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4432
13.3%
e 4394
13.2%
H 2248
 
6.8%
i 2248
 
6.8%
g 2248
 
6.8%
h 2248
 
6.8%
L 2235
 
6.7%
w 2235
 
6.7%
M 2197
 
6.6%
d 2197
 
6.6%
Other values (3) 6591
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4432
13.3%
e 4394
13.2%
H 2248
 
6.8%
i 2248
 
6.8%
g 2248
 
6.8%
h 2248
 
6.8%
L 2235
 
6.7%
w 2235
 
6.7%
M 2197
 
6.6%
d 2197
 
6.6%
Other values (3) 6591
19.8%

Dietary_Intake_Calories
Real number (ℝ)

Distinct2325
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2745.7211
Minimum1500
Maximum3999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-06-30T09:32:48.155099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile1625.9
Q12129
median2729.5
Q33379
95-th percentile3860
Maximum3999
Range2499
Interquartile range (IQR)1250

Descriptive statistics

Standard deviation719.76542
Coefficient of variation (CV)0.26214076
Kurtosis-1.2038878
Mean2745.7211
Median Absolute Deviation (MAD)625.5
Skewness0.013507877
Sum18341417
Variance518062.26
MonotonicityNot monotonic
2025-06-30T09:32:48.770308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1748 10
 
0.1%
1996 9
 
0.1%
2538 9
 
0.1%
2345 9
 
0.1%
3490 9
 
0.1%
3821 9
 
0.1%
3457 8
 
0.1%
3791 8
 
0.1%
2467 8
 
0.1%
2670 8
 
0.1%
Other values (2315) 6593
98.7%
ValueCountFrequency (%)
1500 5
0.1%
1501 2
 
< 0.1%
1502 1
 
< 0.1%
1503 5
0.1%
1504 1
 
< 0.1%
1505 2
 
< 0.1%
1506 3
< 0.1%
1507 4
0.1%
1508 5
0.1%
1509 1
 
< 0.1%
ValueCountFrequency (%)
3999 1
 
< 0.1%
3998 4
0.1%
3997 4
0.1%
3996 5
0.1%
3995 1
 
< 0.1%
3994 2
 
< 0.1%
3993 2
 
< 0.1%
3992 1
 
< 0.1%
3991 1
 
< 0.1%
3990 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size414.3 KiB
Moderate
3373 
Heavy
3307 

Length

Max length8
Median length8
Mean length6.5148204
Min length5

Characters and Unicode

Total characters43,519
Distinct characters10
Distinct categories1 ?
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 rowModerate
2nd rowModerate
3rd rowHeavy
4th rowModerate
5th rowHeavy

Common Values

ValueCountFrequency (%)
Moderate 3373
50.5%
Heavy 3307
49.5%

Length

2025-06-30T09:32:49.400522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T09:32:49.858171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
moderate 3373
50.5%
heavy 3307
49.5%

Most occurring characters

ValueCountFrequency (%)
e 10053
23.1%
a 6680
15.3%
M 3373
 
7.8%
o 3373
 
7.8%
d 3373
 
7.8%
r 3373
 
7.8%
t 3373
 
7.8%
H 3307
 
7.6%
v 3307
 
7.6%
y 3307
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10053
23.1%
a 6680
15.3%
M 3373
 
7.8%
o 3373
 
7.8%
d 3373
 
7.8%
r 3373
 
7.8%
t 3373
 
7.8%
H 3307
 
7.6%
v 3307
 
7.6%
y 3307
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10053
23.1%
a 6680
15.3%
M 3373
 
7.8%
o 3373
 
7.8%
d 3373
 
7.8%
r 3373
 
7.8%
t 3373
 
7.8%
H 3307
 
7.6%
v 3307
 
7.6%
y 3307
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10053
23.1%
a 6680
15.3%
M 3373
 
7.8%
o 3373
 
7.8%
d 3373
 
7.8%
r 3373
 
7.8%
t 3373
 
7.8%
H 3307
 
7.6%
v 3307
 
7.6%
y 3307
 
7.6%

Smoking_Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size411.0 KiB
Former
2231 
Current
2226 
Never
2223 

Length

Max length7
Median length6
Mean length6.0004491
Min length5

Characters and Unicode

Total characters40,083
Distinct characters11
Distinct categories1 ?
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 rowNever
2nd rowCurrent
3rd rowFormer
4th rowNever
5th rowCurrent

Common Values

ValueCountFrequency (%)
Former 2231
33.4%
Current 2226
33.3%
Never 2223
33.3%

Length

2025-06-30T09:32:50.321023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T09:32:50.840210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
former 2231
33.4%
current 2226
33.3%
never 2223
33.3%

Most occurring characters

ValueCountFrequency (%)
r 11137
27.8%
e 8903
22.2%
F 2231
 
5.6%
o 2231
 
5.6%
m 2231
 
5.6%
C 2226
 
5.6%
u 2226
 
5.6%
n 2226
 
5.6%
t 2226
 
5.6%
N 2223
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40083
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 11137
27.8%
e 8903
22.2%
F 2231
 
5.6%
o 2231
 
5.6%
m 2231
 
5.6%
C 2226
 
5.6%
u 2226
 
5.6%
n 2226
 
5.6%
t 2226
 
5.6%
N 2223
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40083
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 11137
27.8%
e 8903
22.2%
F 2231
 
5.6%
o 2231
 
5.6%
m 2231
 
5.6%
C 2226
 
5.6%
u 2226
 
5.6%
n 2226
 
5.6%
t 2226
 
5.6%
N 2223
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40083
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 11137
27.8%
e 8903
22.2%
F 2231
 
5.6%
o 2231
 
5.6%
m 2231
 
5.6%
C 2226
 
5.6%
u 2226
 
5.6%
n 2226
 
5.6%
t 2226
 
5.6%
N 2223
 
5.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size378.4 KiB
1
3415 
0
3265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6,680
Distinct characters2
Distinct categories1 ?
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 row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 3415
51.1%
0 3265
48.9%

Length

2025-06-30T09:32:51.221214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T09:32:51.559407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3415
51.1%
0 3265
48.9%

Most occurring characters

ValueCountFrequency (%)
1 3415
51.1%
0 3265
48.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3415
51.1%
0 3265
48.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3415
51.1%
0 3265
48.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3415
51.1%
0 3265
48.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.8 KiB
1
3442 
0
3238 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6,680
Distinct characters2
Distinct categories1 ?
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 row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 3442
51.5%
0 3238
48.5%

Length

2025-06-30T09:32:51.935443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T09:32:52.346587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3442
51.5%
0 3238
48.5%

Most occurring characters

ValueCountFrequency (%)
1 3442
51.5%
0 3238
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3442
51.5%
0 3238
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3442
51.5%
0 3238
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3442
51.5%
0 3238
48.5%

Interactions

2025-06-30T09:32:28.365392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:35.716706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:39.895594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:44.347958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:48.805560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:54.065530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:58.842075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:03.073344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:07.870172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:12.419692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:16.173811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:20.400100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:24.338799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:28.757268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:36.083498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:40.345140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:44.610173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:49.159530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:54.595551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:59.240558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:03.405427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:08.205415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:12.679056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:16.445135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:20.711574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:24.588585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:29.012393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:36.383380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:40.637825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:45.125604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:49.528089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:54.925203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:59.565093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:03.862421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:08.508630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:12.961038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:16.699294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:21.020170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:24.872340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:29.315570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:36.699577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:40.950615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:45.445541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:49.866699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:55.285173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:59.905272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:04.205428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:08.825397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:13.305326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:17.135136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:21.306943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:25.131315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:29.636588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:37.001445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:41.335491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:45.815136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:50.260237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:55.600988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:00.255258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:04.530552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:09.620311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:13.665481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:17.470201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:21.635149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:25.465416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:30.035175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:37.405767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:41.880241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:46.091338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:50.740607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:56.010759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:00.560151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:04.833749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:09.972584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:13.918822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:17.752682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:21.965326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:25.757331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:30.315144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:37.695170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:42.205765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:46.357256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:51.255564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:56.378612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:00.872419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:05.140348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:10.253296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:14.235219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:18.077976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:22.242637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:26.055060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:30.635121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:37.985518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:42.449096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:46.687746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:51.615165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:56.740595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:01.223555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:05.647720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:10.555115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:14.574011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:18.525107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:22.561103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:26.515065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:30.946573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:38.351143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:42.815346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:47.043385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:52.001302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:57.110426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:01.581371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:06.236208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:10.869001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:14.843129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:18.877626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:22.899418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:26.865432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:31.625488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:38.647445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:43.115175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:47.342042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:52.425229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:57.435301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:01.898007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:06.537235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:11.145658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:15.125227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:19.150093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:23.231580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:27.148281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:31.900358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:38.986301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:43.462747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:47.665561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:52.841513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:57.765190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:02.253007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:06.877398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:11.465478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:15.385155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:19.420218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:23.497962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:27.410874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:32.156239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:39.375144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:43.775539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:48.011407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:53.240242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:58.162562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:02.538506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:07.197376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:11.723992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:15.639827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:19.770211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:23.836627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:27.685337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:32.447789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:39.644106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:44.043024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:48.393341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:53.635613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:31:58.476179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:02.785268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:07.545375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:12.103130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:15.915362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:20.115224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:24.085516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T09:32:27.981822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-30T09:32:52.819792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeAlcohol_ConsumptionBMIBlood_Pressure_DiastolicBlood_Pressure_SystolicCholesterol_HDLCholesterol_LDLCholesterol_TotalDietary_Intake_CaloriesEthnicityFamily_History_of_DiabetesFasting_Blood_GlucoseGGTHbA1cPhysical_Activity_LevelPrevious_Gestational_DiabetesSerum_UrateSexSmoking_StatusWaist_Circumference
Age1.0000.017-0.0020.0160.005-0.0040.0080.004-0.0080.0250.0000.0130.009-0.0020.0140.000-0.0020.0080.0000.013
Alcohol_Consumption0.0171.0000.0000.0410.0200.0000.0060.0000.0000.0190.0180.0000.0180.0000.0000.0000.0120.0000.0000.000
BMI-0.0020.0001.000-0.013-0.0010.0170.0040.0070.0120.0190.000-0.0210.009-0.0020.0280.0000.0010.0000.000-0.015
Blood_Pressure_Diastolic0.0160.041-0.0131.0000.0030.004-0.0200.010-0.0110.0000.0170.005-0.0170.0010.0180.0000.0070.0000.027-0.020
Blood_Pressure_Systolic0.0050.020-0.0010.0031.0000.008-0.005-0.0010.0110.0000.010-0.0020.013-0.0080.0000.000-0.0080.0000.000-0.007
Cholesterol_HDL-0.0040.0000.0170.0040.0081.0000.016-0.030-0.0060.0000.000-0.003-0.012-0.0030.0030.000-0.0070.0160.0250.011
Cholesterol_LDL0.0080.0060.004-0.020-0.0050.0161.000-0.016-0.0170.0200.0000.018-0.0070.0070.0000.0000.0280.0000.028-0.013
Cholesterol_Total0.0040.0000.0070.010-0.001-0.030-0.0161.0000.0140.0000.000-0.0080.019-0.0030.0000.024-0.0060.0000.026-0.004
Dietary_Intake_Calories-0.0080.0000.012-0.0110.011-0.006-0.0170.0141.0000.0000.000-0.021-0.0000.0120.0000.0000.0020.0270.0000.007
Ethnicity0.0250.0190.0190.0000.0000.0000.0200.0000.0001.0000.0000.0090.0000.0000.0000.0320.0000.0200.0240.020
Family_History_of_Diabetes0.0000.0180.0000.0170.0100.0000.0000.0000.0000.0001.0000.0240.0220.0000.0000.0000.0380.0160.0000.011
Fasting_Blood_Glucose0.0130.000-0.0210.005-0.002-0.0030.018-0.008-0.0210.0090.0241.000-0.002-0.0200.0000.0070.0040.0000.0000.006
GGT0.0090.0180.009-0.0170.013-0.012-0.0070.019-0.0000.0000.022-0.0021.000-0.0060.0000.000-0.0010.0000.0000.016
HbA1c-0.0020.000-0.0020.001-0.008-0.0030.007-0.0030.0120.0000.000-0.020-0.0061.0000.0000.000-0.0060.0130.000-0.007
Physical_Activity_Level0.0140.0000.0280.0180.0000.0030.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
Previous_Gestational_Diabetes0.0000.0000.0000.0000.0000.0000.0000.0240.0000.0320.0000.0070.0000.0000.0001.0000.0150.0000.0210.000
Serum_Urate-0.0020.0120.0010.007-0.008-0.0070.028-0.0060.0020.0000.0380.004-0.001-0.0060.0000.0151.0000.0000.000-0.008
Sex0.0080.0000.0000.0000.0000.0160.0000.0000.0270.0200.0160.0000.0000.0130.0000.0000.0001.0000.0320.035
Smoking_Status0.0000.0000.0000.0270.0000.0250.0280.0260.0000.0240.0000.0000.0000.0000.0000.0210.0000.0321.0000.031
Waist_Circumference0.0130.000-0.015-0.020-0.0070.011-0.013-0.0040.0070.0200.0110.0060.016-0.0070.0000.000-0.0080.0350.0311.000

Missing values

2025-06-30T09:32:32.978413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-30T09:32:33.980169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeSexEthnicityBMIWaist_CircumferenceFasting_Blood_GlucoseHbA1cBlood_Pressure_SystolicBlood_Pressure_DiastolicCholesterol_TotalCholesterol_HDLCholesterol_LDLGGTSerum_UratePhysical_Activity_LevelDietary_Intake_CaloriesAlcohol_ConsumptionSmoking_StatusFamily_History_of_DiabetesPrevious_Gestational_Diabetes
058FemaleWhite35.883.4123.910.9152114197.850.299.237.57.2Moderate1538ModerateNever01
148MaleAsian24.171.4183.712.810391261.662.0146.488.56.1Moderate2653ModerateCurrent01
234FemaleBlack25.0113.8142.014.5179104261.032.1164.156.26.9Low1684HeavyFormer10
362MaleAsian32.7100.4167.48.8176118183.441.184.034.45.4Low3796ModerateNever10
427FemaleAsian33.5110.8146.47.112297203.253.992.881.97.4Moderate3161HeavyCurrent00
658MaleBlack33.2100.097.713.313180199.877.973.452.14.7High3107ModerateNever00
738FemaleHispanic26.9105.080.210.912183154.069.7122.272.05.6Moderate2390HeavyCurrent01
930MaleWhite24.074.672.014.014683250.053.3170.714.56.9High2230ModerateFormer10
1030MaleBlack21.986.5124.86.0104111295.453.7182.388.83.8Low2836HeavyCurrent10
1143FemaleHispanic33.4105.593.24.513969283.870.3102.135.88.0Low2166ModerateCurrent11
AgeSexEthnicityBMIWaist_CircumferenceFasting_Blood_GlucoseHbA1cBlood_Pressure_SystolicBlood_Pressure_DiastolicCholesterol_TotalCholesterol_HDLCholesterol_LDLGGTSerum_UratePhysical_Activity_LevelDietary_Intake_CaloriesAlcohol_ConsumptionSmoking_StatusFamily_History_of_DiabetesPrevious_Gestational_Diabetes
998753FemaleBlack35.792.0195.113.615872293.774.1100.216.33.2High3039ModerateNever10
998857MaleWhite24.4119.071.05.815764183.231.3138.273.46.3High2508ModerateCurrent00
998924MaleAsian24.0103.0160.04.313974280.878.8183.778.67.2Moderate2392ModerateFormer10
999135MaleAsian31.777.3187.37.6100119155.930.8132.067.47.8High1706HeavyFormer01
999227FemaleWhite36.0118.2119.514.215761244.855.4110.843.66.5High2708ModerateCurrent01
999344MaleAsian28.270.7137.44.017665262.463.397.460.84.6High3211ModerateNever10
999451MaleHispanic26.7104.1166.512.815695293.147.695.370.74.3Moderate2517HeavyFormer10
999750FemaleAsian29.0106.397.54.912261266.069.8156.185.84.9High3175HeavyFormer11
999862FemaleWhite27.3119.989.011.599115172.374.2110.925.35.2High3478ModerateNever10
999929MaleBlack20.6102.070.814.515664277.938.0108.168.56.0Low2918HeavyFormer10