Overview

Dataset statistics

Number of variables24
Number of observations991346
Missing cells0
Missing cells (%)0.0%
Duplicate rows26
Duplicate rows (%)< 0.1%
Total size in memory181.5 MiB
Average record size in memory192.0 B

Variable types

Categorical5
Numeric19

Alerts

Dataset has 26 (< 0.1%) duplicate rowsDuplicates
height is highly overall correlated with weight and 2 other fieldsHigh correlation
weight is highly overall correlated with height and 3 other fieldsHigh correlation
waistline is highly overall correlated with weightHigh correlation
sight_left is highly overall correlated with sight_rightHigh correlation
sight_right is highly overall correlated with sight_leftHigh correlation
sbp is highly overall correlated with dbpHigh correlation
dbp is highly overall correlated with sbpHigh correlation
tot_chole is highly overall correlated with ldl_choleHigh correlation
ldl_chole is highly overall correlated with tot_choleHigh correlation
hemoglobin is highly overall correlated with height and 2 other fieldsHigh correlation
sgot_ast is highly overall correlated with sgot_altHigh correlation
sgot_alt is highly overall correlated with sgot_ast and 1 other fieldsHigh correlation
gamma_gtp is highly overall correlated with sgot_altHigh correlation
sex is highly overall correlated with height and 3 other fieldsHigh correlation
hear_left is highly overall correlated with hear_rightHigh correlation
hear_right is highly overall correlated with hear_leftHigh correlation
smk_stat_type_cd is highly overall correlated with sexHigh correlation
hear_left is highly imbalanced (79.8%)Imbalance
hear_right is highly imbalanced (80.3%)Imbalance
waistline is highly skewed (γ1 = 26.78843978)Skewed
hdl_chole is highly skewed (γ1 = 104.5776351)Skewed
serum_creatinine is highly skewed (γ1 = 111.022058)Skewed
sgot_ast is highly skewed (γ1 = 150.4916897)Skewed
sgot_alt is highly skewed (γ1 = 50.03887229)Skewed

Reproduction

Analysis started2023-11-25 02:29:24.903586
Analysis finished2023-11-25 02:31:32.778612
Duration2 minutes and 7.88 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Male
526415 
Female
464931 

Length

Max length6
Median length4
Mean length4.9379793
Min length4

Characters and Unicode

Total characters4895246
Distinct characters6
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 rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 526415
53.1%
Female 464931
46.9%

Length

2023-11-24T21:31:32.841210image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T21:31:32.962773image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
male 526415
53.1%
female 464931
46.9%

Most occurring characters

ValueCountFrequency (%)
e 1456277
29.7%
a 991346
20.3%
l 991346
20.3%
M 526415
 
10.8%
F 464931
 
9.5%
m 464931
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3903900
79.7%
Uppercase Letter 991346
 
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1456277
37.3%
a 991346
25.4%
l 991346
25.4%
m 464931
 
11.9%
Uppercase Letter
ValueCountFrequency (%)
M 526415
53.1%
F 464931
46.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4895246
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1456277
29.7%
a 991346
20.3%
l 991346
20.3%
M 526415
 
10.8%
F 464931
 
9.5%
m 464931
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4895246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1456277
29.7%
a 991346
20.3%
l 991346
20.3%
M 526415
 
10.8%
F 464931
 
9.5%
m 464931
 
9.5%

age
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.614491
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:33.045309image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q135
median45
Q360
95-th percentile70
Maximum85
Range65
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.181339
Coefficient of variation (CV)0.29783662
Kurtosis-0.57561552
Mean47.614491
Median Absolute Deviation (MAD)10
Skewness0.15365339
Sum47202435
Variance201.11038
MonotonicityNot monotonic
2023-11-24T21:31:33.144803image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
40 130385
13.2%
50 129434
13.1%
45 118355
11.9%
55 111223
11.2%
60 106063
10.7%
35 84726
8.5%
30 77600
7.8%
25 64370
6.5%
65 52961
5.3%
70 50666
 
5.1%
Other values (4) 65563
6.6%
ValueCountFrequency (%)
20 21971
 
2.2%
25 64370
6.5%
30 77600
7.8%
35 84726
8.5%
40 130385
13.2%
45 118355
11.9%
50 129434
13.1%
55 111223
11.2%
60 106063
10.7%
65 52961
5.3%
ValueCountFrequency (%)
85 3291
 
0.3%
80 14968
 
1.5%
75 25333
 
2.6%
70 50666
 
5.1%
65 52961
5.3%
60 106063
10.7%
55 111223
11.2%
50 129434
13.1%
45 118355
11.9%
40 130385
13.2%

height
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.24063
Minimum130
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:33.252361image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile145
Q1155
median160
Q3170
95-th percentile175
Maximum190
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.2829575
Coefficient of variation (CV)0.057217219
Kurtosis-0.53564034
Mean162.24063
Median Absolute Deviation (MAD)5
Skewness-0.02273717
Sum1.608366 × 108
Variance86.173299
MonotonicityNot monotonic
2023-11-24T21:31:33.348457image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
160 181809
18.3%
165 178228
18.0%
170 166328
16.8%
155 165678
16.7%
150 107929
10.9%
175 98850
10.0%
145 39176
 
4.0%
180 35970
 
3.6%
140 9100
 
0.9%
185 6588
 
0.7%
Other values (3) 1690
 
0.2%
ValueCountFrequency (%)
130 86
 
< 0.1%
135 1241
 
0.1%
140 9100
 
0.9%
145 39176
 
4.0%
150 107929
10.9%
155 165678
16.7%
160 181809
18.3%
165 178228
18.0%
170 166328
16.8%
175 98850
10.0%
ValueCountFrequency (%)
190 363
 
< 0.1%
185 6588
 
0.7%
180 35970
 
3.6%
175 98850
10.0%
170 166328
16.8%
165 178228
18.0%
160 181809
18.3%
155 165678
16.7%
150 107929
10.9%
145 39176
 
4.0%

weight
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.28405
Minimum25
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:33.463955image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile45
Q155
median60
Q370
95-th percentile85
Maximum140
Range115
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.514241
Coefficient of variation (CV)0.19774715
Kurtosis0.35922025
Mean63.28405
Median Absolute Deviation (MAD)10
Skewness0.5765566
Sum62736390
Variance156.60622
MonotonicityNot monotonic
2023-11-24T21:31:33.579441image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
60 151134
15.2%
55 150415
15.2%
65 141241
14.2%
50 125079
12.6%
70 122281
12.3%
75 90207
9.1%
45 63047
6.4%
80 58176
 
5.9%
85 33708
 
3.4%
90 18250
 
1.8%
Other values (14) 37808
 
3.8%
ValueCountFrequency (%)
25 9
 
< 0.1%
30 157
 
< 0.1%
35 1948
 
0.2%
40 16639
 
1.7%
45 63047
6.4%
50 125079
12.6%
55 150415
15.2%
60 151134
15.2%
65 141241
14.2%
70 122281
12.3%
ValueCountFrequency (%)
140 3
 
< 0.1%
135 5
 
< 0.1%
130 43
 
< 0.1%
125 80
 
< 0.1%
120 236
 
< 0.1%
115 573
 
0.1%
110 1177
 
0.1%
105 2454
 
0.2%
100 4829
0.5%
95 9655
1.0%

waistline
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct737
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.233358
Minimum8
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:33.723817image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile66
Q174.1
median81
Q387.8
95-th percentile97
Maximum999
Range991
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation11.850323
Coefficient of variation (CV)0.14588001
Kurtosis2066.8122
Mean81.233358
Median Absolute Deviation (MAD)6.8
Skewness26.78844
Sum80530364
Variance140.43016
MonotonicityNot monotonic
2023-11-24T21:31:33.867612image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 37790
 
3.8%
81 34603
 
3.5%
82 34024
 
3.4%
84 33913
 
3.4%
86 32723
 
3.3%
83 32282
 
3.3%
76 31254
 
3.2%
78 30832
 
3.1%
85 30626
 
3.1%
79 28853
 
2.9%
Other values (727) 664446
67.0%
ValueCountFrequency (%)
8 1
 
< 0.1%
27 1
 
< 0.1%
30 2
< 0.1%
32 3
< 0.1%
35 2
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
43 1
 
< 0.1%
48 1
 
< 0.1%
49 1
 
< 0.1%
ValueCountFrequency (%)
999 57
< 0.1%
149.1 1
 
< 0.1%
145 1
 
< 0.1%
140 1
 
< 0.1%
138 1
 
< 0.1%
136.8 1
 
< 0.1%
136 2
 
< 0.1%
135 1
 
< 0.1%
134 3
 
< 0.1%
133 1
 
< 0.1%

sight_left
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98083434
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:33.994750image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.60594863
Coefficient of variation (CV)0.61778897
Kurtosis144.94968
Mean0.98083434
Median Absolute Deviation (MAD)0.2
Skewness9.994626
Sum972346.2
Variance0.36717375
MonotonicityNot monotonic
2023-11-24T21:31:34.106956image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 201418
20.3%
1.2 188460
19.0%
1.5 121713
12.3%
0.9 105297
10.6%
0.8 99913
10.1%
0.7 83749
8.4%
0.6 53644
 
5.4%
0.5 51895
 
5.2%
0.4 30744
 
3.1%
0.3 20388
 
2.1%
Other values (14) 34125
 
3.4%
ValueCountFrequency (%)
0.1 9503
 
1.0%
0.2 12255
 
1.2%
0.3 20388
 
2.1%
0.4 30744
 
3.1%
0.5 51895
 
5.2%
0.6 53644
 
5.4%
0.7 83749
8.4%
0.8 99913
10.1%
0.9 105297
10.6%
1 201418
20.3%
ValueCountFrequency (%)
9.9 3118
 
0.3%
2.5 7
 
< 0.1%
2.2 2
 
< 0.1%
2.1 3
 
< 0.1%
2 8452
 
0.9%
1.9 32
 
< 0.1%
1.8 25
 
< 0.1%
1.7 14
 
< 0.1%
1.6 371
 
< 0.1%
1.5 121713
12.3%

sight_right
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97842913
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:34.222600image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.60477411
Coefficient of variation (CV)0.61810722
Kurtosis145.92255
Mean0.97842913
Median Absolute Deviation (MAD)0.2
Skewness10.033647
Sum969961.8
Variance0.36575173
MonotonicityNot monotonic
2023-11-24T21:31:34.334737image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 204493
20.6%
1.2 187266
18.9%
1.5 120620
12.2%
0.9 106186
10.7%
0.8 98777
10.0%
0.7 84168
8.5%
0.6 53238
 
5.4%
0.5 50803
 
5.1%
0.4 31318
 
3.2%
0.3 20090
 
2.0%
Other values (14) 34387
 
3.5%
ValueCountFrequency (%)
0.1 10028
 
1.0%
0.2 13002
 
1.3%
0.3 20090
 
2.0%
0.4 31318
 
3.2%
0.5 50803
 
5.1%
0.6 53238
 
5.4%
0.7 84168
8.5%
0.8 98777
10.0%
0.9 106186
10.7%
1 204493
20.6%
ValueCountFrequency (%)
9.9 3111
 
0.3%
2.5 10
 
< 0.1%
2.2 1
 
< 0.1%
2.1 10
 
< 0.1%
2 7363
 
0.7%
1.9 21
 
< 0.1%
1.8 32
 
< 0.1%
1.7 24
 
< 0.1%
1.6 390
 
< 0.1%
1.5 120620
12.2%

hear_left
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1.0
960124 
2.0
 
31222

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2974038
Distinct characters4
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 row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 960124
96.9%
2.0 31222
 
3.1%

Length

2023-11-24T21:31:34.441612image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T21:31:34.786843image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 960124
96.9%
2.0 31222
 
3.1%

Most occurring characters

ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 960124
32.3%
2 31222
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982692
66.7%
Other Punctuation 991346
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 991346
50.0%
1 960124
48.4%
2 31222
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 991346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2974038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 960124
32.3%
2 31222
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2974038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 960124
32.3%
2 31222
 
1.0%

hear_right
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1.0
961134 
2.0
 
30212

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2974038
Distinct characters4
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 row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 961134
97.0%
2.0 30212
 
3.0%

Length

2023-11-24T21:31:34.869421image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T21:31:34.973009image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 961134
97.0%
2.0 30212
 
3.0%

Most occurring characters

ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 961134
32.3%
2 30212
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982692
66.7%
Other Punctuation 991346
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 991346
50.0%
1 961134
48.5%
2 30212
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 991346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2974038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 961134
32.3%
2 30212
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2974038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 961134
32.3%
2 30212
 
1.0%

sbp
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.4325
Minimum67
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:35.083739image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile100
Q1112
median120
Q3131
95-th percentile148
Maximum273
Range206
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.543148
Coefficient of variation (CV)0.11878503
Kurtosis0.99663922
Mean122.4325
Median Absolute Deviation (MAD)10
Skewness0.48206032
Sum1.2137297 × 108
Variance211.50315
MonotonicityNot monotonic
2023-11-24T21:31:35.216307image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 78786
 
7.9%
110 72193
 
7.3%
130 71714
 
7.2%
118 40078
 
4.0%
100 30829
 
3.1%
138 24426
 
2.5%
119 24166
 
2.4%
128 23766
 
2.4%
124 22224
 
2.2%
116 22177
 
2.2%
Other values (161) 580987
58.6%
ValueCountFrequency (%)
67 1
 
< 0.1%
70 3
 
< 0.1%
72 1
 
< 0.1%
73 4
 
< 0.1%
74 3
 
< 0.1%
75 8
< 0.1%
76 7
< 0.1%
77 6
< 0.1%
78 11
< 0.1%
79 6
< 0.1%
ValueCountFrequency (%)
273 1
< 0.1%
270 1
< 0.1%
255 1
< 0.1%
253 1
< 0.1%
244 1
< 0.1%
241 1
< 0.1%
240 1
< 0.1%
238 1
< 0.1%
236 1
< 0.1%
235 1
< 0.1%

dbp
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.052627
Minimum32
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:35.353250image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile60
Q170
median76
Q382
95-th percentile92
Maximum185
Range153
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.8893654
Coefficient of variation (CV)0.13003318
Kurtosis0.89150383
Mean76.052627
Median Absolute Deviation (MAD)6
Skewness0.4000338
Sum75394468
Variance97.799547
MonotonicityNot monotonic
2023-11-24T21:31:35.487939image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 123156
 
12.4%
70 111699
 
11.3%
78 44628
 
4.5%
60 41253
 
4.2%
72 33644
 
3.4%
75 32575
 
3.3%
76 31976
 
3.2%
74 31773
 
3.2%
82 27195
 
2.7%
90 25959
 
2.6%
Other values (117) 487488
49.2%
ValueCountFrequency (%)
32 1
 
< 0.1%
33 1
 
< 0.1%
34 1
 
< 0.1%
36 2
 
< 0.1%
37 3
 
< 0.1%
38 1
 
< 0.1%
39 3
 
< 0.1%
40 14
< 0.1%
41 7
< 0.1%
42 12
< 0.1%
ValueCountFrequency (%)
185 1
< 0.1%
181 1
< 0.1%
180 1
< 0.1%
170 1
< 0.1%
164 1
< 0.1%
163 1
< 0.1%
160 2
< 0.1%
156 2
< 0.1%
154 2
< 0.1%
153 2
< 0.1%

blds
Real number (ℝ)

Distinct498
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.42445
Minimum25
Maximum852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:35.641068image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile79
Q188
median96
Q3105
95-th percentile137
Maximum852
Range827
Interquartile range (IQR)17

Descriptive statistics

Standard deviation24.17996
Coefficient of variation (CV)0.24077762
Kurtosis40.470487
Mean100.42445
Median Absolute Deviation (MAD)8
Skewness4.6173775
Sum99555374
Variance584.67045
MonotonicityNot monotonic
2023-11-24T21:31:35.776619image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 35243
 
3.6%
92 35227
 
3.6%
95 35190
 
3.5%
94 35173
 
3.5%
91 34389
 
3.5%
96 33814
 
3.4%
90 33754
 
3.4%
97 32981
 
3.3%
89 32178
 
3.2%
98 31902
 
3.2%
Other values (488) 651495
65.7%
ValueCountFrequency (%)
25 1
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
33 2
< 0.1%
34 2
< 0.1%
36 2
< 0.1%
37 1
 
< 0.1%
38 4
< 0.1%
39 1
 
< 0.1%
40 1
 
< 0.1%
ValueCountFrequency (%)
852 1
< 0.1%
801 1
< 0.1%
800 1
< 0.1%
784 1
< 0.1%
769 1
< 0.1%
741 1
< 0.1%
685 1
< 0.1%
663 1
< 0.1%
638 1
< 0.1%
629 2
< 0.1%

tot_chole
Real number (ℝ)

HIGH CORRELATION 

Distinct474
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.55702
Minimum30
Maximum2344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:35.912557image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile137
Q1169
median193
Q3219
95-th percentile261
Maximum2344
Range2314
Interquartile range (IQR)50

Descriptive statistics

Standard deviation38.660155
Coefficient of variation (CV)0.19769249
Kurtosis49.462386
Mean195.55702
Median Absolute Deviation (MAD)25
Skewness1.5568817
Sum1.9386467 × 108
Variance1494.6076
MonotonicityNot monotonic
2023-11-24T21:31:36.048917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 11079
 
1.1%
184 10873
 
1.1%
189 10857
 
1.1%
190 10825
 
1.1%
188 10796
 
1.1%
197 10775
 
1.1%
187 10746
 
1.1%
192 10746
 
1.1%
196 10723
 
1.1%
186 10717
 
1.1%
Other values (464) 883209
89.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
45 1
 
< 0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
57 3
< 0.1%
58 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
62 1
 
< 0.1%
63 2
< 0.1%
ValueCountFrequency (%)
2344 1
< 0.1%
2196 1
< 0.1%
2067 1
< 0.1%
2046 1
< 0.1%
2033 1
< 0.1%
1815 1
< 0.1%
1736 1
< 0.1%
1619 1
< 0.1%
1605 1
< 0.1%
1575 1
< 0.1%

hdl_chole
Real number (ℝ)

SKEWED 

Distinct223
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.9368
Minimum1
Maximum8110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:36.190382image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q146
median55
Q366
95-th percentile84
Maximum8110
Range8109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.238479
Coefficient of variation (CV)0.30276515
Kurtosis48094.155
Mean56.9368
Median Absolute Deviation (MAD)10
Skewness104.57764
Sum56444069
Variance297.16516
MonotonicityNot monotonic
2023-11-24T21:31:36.334560image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 29602
 
3.0%
52 28335
 
2.9%
53 28323
 
2.9%
51 28126
 
2.8%
54 27952
 
2.8%
49 27869
 
2.8%
48 27428
 
2.8%
55 27092
 
2.7%
56 26827
 
2.7%
47 26476
 
2.7%
Other values (213) 713316
72.0%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 7
< 0.1%
3 3
 
< 0.1%
4 5
 
< 0.1%
5 2
 
< 0.1%
6 6
 
< 0.1%
7 12
< 0.1%
8 6
 
< 0.1%
9 11
< 0.1%
10 15
< 0.1%
ValueCountFrequency (%)
8110 1
< 0.1%
1206 1
< 0.1%
933 1
< 0.1%
797 1
< 0.1%
727 1
< 0.1%
701 1
< 0.1%
697 1
< 0.1%
677 1
< 0.1%
658 1
< 0.1%
636 1
< 0.1%

ldl_chole
Real number (ℝ)

HIGH CORRELATION 

Distinct432
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.03769
Minimum1
Maximum5119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:36.487950image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60
Q189
median111
Q3135
95-th percentile172
Maximum5119
Range5118
Interquartile range (IQR)46

Descriptive statistics

Standard deviation35.842812
Coefficient of variation (CV)0.31708726
Kurtosis481.28298
Mean113.03769
Median Absolute Deviation (MAD)23
Skewness5.2517394
Sum1.1205946 × 108
Variance1284.7072
MonotonicityNot monotonic
2023-11-24T21:31:36.633518image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109 11824
 
1.2%
104 11795
 
1.2%
107 11782
 
1.2%
110 11773
 
1.2%
102 11740
 
1.2%
112 11656
 
1.2%
115 11631
 
1.2%
108 11611
 
1.2%
105 11607
 
1.2%
106 11597
 
1.2%
Other values (422) 874330
88.2%
ValueCountFrequency (%)
1 81
< 0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 11
 
< 0.1%
5 20
 
< 0.1%
6 23
 
< 0.1%
7 29
 
< 0.1%
8 40
< 0.1%
9 31
 
< 0.1%
10 39
< 0.1%
ValueCountFrequency (%)
5119 1
< 0.1%
2254 1
< 0.1%
2114 1
< 0.1%
2111 1
< 0.1%
2043 1
< 0.1%
2026 1
< 0.1%
1933 1
< 0.1%
1798 1
< 0.1%
1750 1
< 0.1%
1696 1
< 0.1%

triglyceride
Real number (ℝ)

Distinct1657
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.14175
Minimum1
Maximum9490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:36.780488image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q173
median106
Q3159
95-th percentile297
Maximum9490
Range9489
Interquartile range (IQR)86

Descriptive statistics

Standard deviation102.19698
Coefficient of variation (CV)0.77338906
Kurtosis175.38524
Mean132.14175
Median Absolute Deviation (MAD)39
Skewness6.5293729
Sum1.309982 × 108
Variance10444.224
MonotonicityNot monotonic
2023-11-24T21:31:36.915246image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 8236
 
0.8%
78 8207
 
0.8%
79 8178
 
0.8%
69 8139
 
0.8%
70 8131
 
0.8%
76 8122
 
0.8%
68 8120
 
0.8%
82 8102
 
0.8%
75 8096
 
0.8%
77 8095
 
0.8%
Other values (1647) 909920
91.8%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 10
< 0.1%
8 7
< 0.1%
9 11
< 0.1%
10 8
< 0.1%
ValueCountFrequency (%)
9490 1
< 0.1%
6430 1
< 0.1%
6173 1
< 0.1%
5236 1
< 0.1%
4164 1
< 0.1%
4000 1
< 0.1%
3858 1
< 0.1%
3848 1
< 0.1%
3830 1
< 0.1%
3771 1
< 0.1%

hemoglobin
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.229824
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:37.052206image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.7
Q113.2
median14.3
Q315.4
95-th percentile16.6
Maximum25
Range24
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.5849287
Coefficient of variation (CV)0.11138077
Kurtosis0.71137942
Mean14.229824
Median Absolute Deviation (MAD)1.1
Skewness-0.3839878
Sum14106679
Variance2.5119991
MonotonicityNot monotonic
2023-11-24T21:31:37.197633image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5 23297
 
2.4%
14 23108
 
2.3%
13.6 23093
 
2.3%
13.4 22946
 
2.3%
13.8 22781
 
2.3%
13.3 22734
 
2.3%
13.9 22635
 
2.3%
15 22600
 
2.3%
13.7 22591
 
2.3%
14.8 22181
 
2.2%
Other values (180) 763380
77.0%
ValueCountFrequency (%)
1 3
< 0.1%
2.8 1
 
< 0.1%
3.7 3
< 0.1%
3.8 1
 
< 0.1%
3.9 3
< 0.1%
4 4
< 0.1%
4.1 2
< 0.1%
4.2 4
< 0.1%
4.3 3
< 0.1%
4.4 2
< 0.1%
ValueCountFrequency (%)
25 2
< 0.1%
24.2 1
< 0.1%
23.9 1
< 0.1%
23.6 1
< 0.1%
23.3 1
< 0.1%
22.7 1
< 0.1%
22.1 1
< 0.1%
22 1
< 0.1%
21.8 1
< 0.1%
21.7 2
< 0.1%

urine_protein
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0942244
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:37.307044image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43772355
Coefficient of variation (CV)0.40003087
Kurtosis36.899552
Mean1.0942244
Median Absolute Deviation (MAD)0
Skewness5.6724908
Sum1084755
Variance0.19160191
MonotonicityNot monotonic
2023-11-24T21:31:37.402403image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 935175
94.3%
2 30850
 
3.1%
3 16405
 
1.7%
4 6427
 
0.6%
5 1977
 
0.2%
6 512
 
0.1%
ValueCountFrequency (%)
1 935175
94.3%
2 30850
 
3.1%
3 16405
 
1.7%
4 6427
 
0.6%
5 1977
 
0.2%
6 512
 
0.1%
ValueCountFrequency (%)
6 512
 
0.1%
5 1977
 
0.2%
4 6427
 
0.6%
3 16405
 
1.7%
2 30850
 
3.1%
1 935175
94.3%

serum_creatinine
Real number (ℝ)

SKEWED 

Distinct183
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86046668
Minimum0.1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:37.531677image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.7
median0.8
Q31
95-th percentile1.2
Maximum98
Range97.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.48053042
Coefficient of variation (CV)0.55845326
Kurtosis19089.83
Mean0.86046668
Median Absolute Deviation (MAD)0.1
Skewness111.02206
Sum853020.2
Variance0.23090948
MonotonicityNot monotonic
2023-11-24T21:31:37.674537image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 194902
19.7%
0.9 180626
18.2%
0.7 164293
16.6%
1 140743
14.2%
0.6 109236
11.0%
1.1 86355
8.7%
1.2 40744
 
4.1%
0.5 38932
 
3.9%
1.3 15160
 
1.5%
0.4 6050
 
0.6%
Other values (173) 14305
 
1.4%
ValueCountFrequency (%)
0.1 425
 
< 0.1%
0.2 99
 
< 0.1%
0.3 597
 
0.1%
0.4 6050
 
0.6%
0.5 38932
 
3.9%
0.6 109236
11.0%
0.7 164293
16.6%
0.8 194902
19.7%
0.9 180626
18.2%
1 140743
14.2%
ValueCountFrequency (%)
98 2
< 0.1%
96 2
< 0.1%
95 1
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%

sgot_ast
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct568
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.989308
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-11-24T21:31:37.821619image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q119
median23
Q328
95-th percentile46
Maximum9999
Range9998
Interquartile range (IQR)9

Descriptive statistics

Standard deviation23.493386
Coefficient of variation (CV)0.90396349
Kurtosis50432.651
Mean25.989308
Median Absolute Deviation (MAD)5
Skewness150.49169
Sum25764397
Variance551.93919
MonotonicityNot monotonic
2023-11-24T21:31:37.963202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/