Overview

Dataset statistics

Number of variables20
Number of observations368
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)3.0%
Total size in memory57.6 KiB
Average record size in memory160.3 B

Variable types

NUM13
CAT7

Reproduction

Analysis started2020-08-25 01:24:58.446890
Analysis finished2020-08-25 01:25:26.227820
Duration27.78 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 11 (3.0%) duplicate rows Duplicates
rectal examination has 68 (18.5%) zeros Zeros
temperature of extremities has 95 (25.8%) zeros Zeros
abdominal distension has 101 (27.4%) zeros Zeros
pulse has 13 (3.5%) zeros Zeros
respiratory rate has 4 (1.1%) zeros Zeros
abdomen has 31 (8.4%) zeros Zeros
pain has 49 (13.3%) zeros Zeros
mucous membranes has 98 (26.6%) zeros Zeros
peristalsis has 49 (13.3%) zeros Zeros

Variables

surgery
Categorical

Distinct count3
Unique (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
214
1
152
2
 
2
ValueCountFrequency (%) 
021458.2%
 
115241.3%
 
220.5%
 
2020-08-25T01:25:26.308493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
021458.2%
 
115241.3%
 
220.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
021458.2%
 
115241.3%
 
220.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
021458.2%
 
115241.3%
 
220.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
021458.2%
 
115241.3%
 
220.5%
 
Distinct count4
Unique (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
141
3
133
2
49
1
45
ValueCountFrequency (%) 
014138.3%
 
313336.1%
 
24913.3%
 
14512.2%
 
2020-08-25T01:25:26.447230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
014138.3%
 
313336.1%
 
24913.3%
 
14512.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
014138.3%
 
313336.1%
 
24913.3%
 
14512.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
014138.3%
 
313336.1%
 
24913.3%
 
14512.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
014138.3%
 
313336.1%
 
24913.3%
 
14512.2%
 
Distinct count4
Unique (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
232
1
96
3
 
38
2
 
2
ValueCountFrequency (%) 
023263.0%
 
19626.1%
 
33810.3%
 
220.5%
 
2020-08-25T01:25:26.583916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
023263.0%
 
19626.1%
 
33810.3%
 
220.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
023263.0%
 
19626.1%
 
33810.3%
 
220.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
023263.0%
 
19626.1%
 
33810.3%
 
220.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
023263.0%
 
19626.1%
 
33810.3%
 
220.5%
 

Age
Categorical

Distinct count2
Unique (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
1
340
9
 
28
ValueCountFrequency (%) 
134092.4%
 
9287.6%
 
2020-08-25T01:25:26.731843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
134092.4%
 
9287.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
134092.4%
 
9287.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
134092.4%
 
9287.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
134092.4%
 
9287.6%
 

rectal examination
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.551630434782609
Minimum0
Maximum4
Zeros68
Zeros (%)18.5%
Memory size3.0 KiB
2020-08-25T01:25:26.844608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.460673412
Coefficient of variation (CV)0.572447088
Kurtosis-0.8728684877
Mean2.551630435
Median Absolute Deviation (MAD)1
Skewness-0.6985231946
Sum939
Variance2.133566817
2020-08-25T01:25:26.958214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
412834.8%
 
39726.4%
 
06818.5%
 
26116.6%
 
1143.8%
 
ValueCountFrequency (%) 
06818.5%
 
1143.8%
 
26116.6%
 
39726.4%
 
412834.8%
 
ValueCountFrequency (%) 
412834.8%
 
39726.4%
 
26116.6%
 
1143.8%
 
06818.5%
 

temperature of extremities
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8233695652173914
Minimum0
Maximum4
Zeros95
Zeros (%)25.8%
Memory size3.0 KiB
2020-08-25T01:25:27.080713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.382568614
Coefficient of variation (CV)0.7582492547
Kurtosis-1.059638845
Mean1.823369565
Median Absolute Deviation (MAD)1
Skewness0.1334782462
Sum671
Variance1.911495972
2020-08-25T01:25:27.191422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
213536.7%
 
09525.8%
 
46517.7%
 
13910.6%
 
3349.2%
 
ValueCountFrequency (%) 
09525.8%
 
13910.6%
 
213536.7%
 
3349.2%
 
46517.7%
 
ValueCountFrequency (%) 
46517.7%
 
3349.2%
 
213536.7%
 
13910.6%
 
09525.8%
 

abdomcentesis total protein
Real number (ℝ≥0)

Distinct count45
Unique (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.057065217391305
Minimum0
Maximum44
Zeros1
Zeros (%)0.3%
Memory size3.0 KiB
2020-08-25T01:25:27.314346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q123
median44
Q344
95-th percentile44
Maximum44
Range44
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.06007965
Coefficient of variation (CV)0.4422013333
Kurtosis-0.5106037499
Mean34.05706522
Median Absolute Deviation (MAD)0
Skewness-1.08555166
Sum12533
Variance226.805999
2020-08-25T01:25:27.411083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4423563.9%
 
11339.0%
 
2215.7%
 
2651.4%
 
3441.1%
 
1641.1%
 
1741.1%
 
2330.8%
 
2430.8%
 
2830.8%
 
530.8%
 
4130.8%
 
4320.5%
 
1420.5%
 
1520.5%
 
320.5%
 
1920.5%
 
2020.5%
 
3820.5%
 
3620.5%
 
2720.5%
 
1220.5%
 
3020.5%
 
3120.5%
 
720.5%
 
Other values (20)215.7%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
2215.7%
 
320.5%
 
410.3%
 
530.8%
 
610.3%
 
720.5%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
4423563.9%
 
4320.5%
 
4210.3%
 
4130.8%
 
4010.3%
 
3910.3%
 
3820.5%
 
3710.3%
 
3620.5%
 
3510.3%
 

abdominal distension
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7146739130434783
Minimum0
Maximum4
Zeros101
Zeros (%)27.4%
Memory size3.0 KiB
2020-08-25T01:25:27.515916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.430535771
Coefficient of variation (CV)0.8342902751
Kurtosis-1.185553698
Mean1.714673913
Median Absolute Deviation (MAD)1
Skewness0.3092248598
Sum631
Variance2.046432591
2020-08-25T01:25:27.623780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
010127.4%
 
28523.1%
 
17520.4%
 
46517.7%
 
34211.4%
 
ValueCountFrequency (%) 
010127.4%
 
17520.4%
 
28523.1%
 
34211.4%
 
46517.7%
 
ValueCountFrequency (%) 
46517.7%
 
34211.4%
 
28523.1%
 
17520.4%
 
010127.4%
 

pulse
Real number (ℝ≥0)

ZEROS

Distinct count55
Unique (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.589673913043477
Minimum0
Maximum54
Zeros13
Zeros (%)3.5%
Memory size3.0 KiB
2020-08-25T01:25:27.938662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q124
median32
Q343
95-th percentile54
Maximum54
Range54
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.7316635
Coefficient of variation (CV)0.4663442725
Kurtosis-0.4220147598
Mean31.58967391
Median Absolute Deviation (MAD)9
Skewness-0.4414986853
Sum11625
Variance217.0219094
2020-08-25T01:25:28.045127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
28359.5%
 
35339.0%
 
54267.1%
 
23215.7%
 
25164.3%
 
24154.1%
 
49154.1%
 
31143.8%
 
41133.5%
 
0133.5%
 
6123.3%
 
45113.0%
 
47113.0%
 
3292.4%
 
3492.4%
 
5282.2%
 
3682.2%
 
3871.9%
 
3061.6%
 
4351.4%
 
5151.4%
 
4451.4%
 
3951.4%
 
151.4%
 
4041.1%
 
Other values (30)5715.5%
 
ValueCountFrequency (%) 
0133.5%
 
151.4%
 
230.8%
 
310.3%
 
430.8%
 
530.8%
 
6123.3%
 
720.5%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
54267.1%
 
5310.3%
 
5282.2%
 
5151.4%
 
5041.1%
 
49154.1%
 
4830.8%
 
47113.0%
 
4620.5%
 
45113.0%
 

respiratory rate
Real number (ℝ≥0)

ZEROS

Distinct count41
Unique (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.779891304347824
Minimum0
Maximum40
Zeros4
Zeros (%)1.1%
Memory size3.0 KiB
2020-08-25T01:25:28.157265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median15
Q329.25
95-th percentile40
Maximum40
Range40
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation13.29390789
Coefficient of variation (CV)0.7078799164
Kurtosis-1.100531028
Mean18.7798913
Median Absolute Deviation (MAD)8
Skewness0.4772679485
Sum6911
Variance176.7279869
2020-08-25T01:25:28.271513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
407119.3%
 
7359.5%
 
11308.2%
 
1277.3%
 
15267.1%
 
5267.1%
 
20226.0%
 
19205.4%
 
14164.3%
 
16123.3%
 
6102.7%
 
28102.7%
 
2361.6%
 
2251.4%
 
341.1%
 
041.1%
 
2130.8%
 
3930.8%
 
3030.8%
 
1830.8%
 
3430.8%
 
2420.5%
 
3720.5%
 
920.5%
 
3520.5%
 
Other values (16)215.7%
 
ValueCountFrequency (%) 
041.1%
 
1277.3%
 
210.3%
 
341.1%
 
410.3%
 
5267.1%
 
6102.7%
 
7359.5%
 
820.5%
 
920.5%
 
ValueCountFrequency (%) 
407119.3%
 
3930.8%
 
3820.5%
 
3720.5%
 
3610.3%
 
3520.5%
 
3430.8%
 
3310.3%
 
3220.5%
 
3120.5%
 

packed cell volume
Real number (ℝ≥0)

Distinct count55
Unique (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.467391304347824
Minimum0
Maximum54
Zeros1
Zeros (%)0.3%
Memory size3.0 KiB
2020-08-25T01:25:28.401644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q115.75
median23
Q335
95-th percentile54
Maximum54
Range54
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation13.9841881
Coefficient of variation (CV)0.5283553616
Kurtosis-0.520642263
Mean26.4673913
Median Absolute Deviation (MAD)9
Skewness0.6369168649
Sum9740
Variance195.5575169
2020-08-25T01:25:28.507463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
543710.1%
 
13226.0%
 
21174.6%
 
23174.6%
 
22174.6%
 
28164.3%
 
11164.3%
 
18143.8%
 
12133.5%
 
25133.5%
 
26113.0%
 
20113.0%
 
19102.7%
 
15102.7%
 
9102.7%
 
2492.4%
 
3992.4%
 
1692.4%
 
3282.2%
 
3071.9%
 
2771.9%
 
3361.6%
 
3161.6%
 
3561.6%
 
4361.6%
 
Other values (30)6116.6%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
530.8%
 
620.5%
 
710.3%
 
841.1%
 
9102.7%
 
ValueCountFrequency (%) 
543710.1%
 
5320.5%
 
5220.5%
 
5120.5%
 
5010.3%
 
4910.3%
 
4810.3%
 
4720.5%
 
4641.1%
 
4510.3%
 

nasogastric tube
Categorical

Distinct count4
Unique (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
3
131
1
121
0
89
2
27
ValueCountFrequency (%) 
313135.6%
 
112132.9%
 
08924.2%
 
2277.3%
 
2020-08-25T01:25:28.653198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
313135.6%
 
112132.9%
 
08924.2%
 
2277.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
313135.6%
 
112132.9%
 
08924.2%
 
2277.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
313135.6%
 
112132.9%
 
08924.2%
 
2277.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
313135.6%
 
112132.9%
 
08924.2%
 
2277.3%
 

total protein
Real number (ℝ≥0)

Distinct count85
Unique (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.20652173913044
Minimum0
Maximum84
Zeros1
Zeros (%)0.3%
Memory size3.0 KiB
2020-08-25T01:25:28.765871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q130
median44.5
Q362
95-th percentile84
Maximum84
Range84
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.00311499
Coefficient of variation (CV)0.4761906795
Kurtosis-0.8284511848
Mean46.20652174
Median Absolute Deviation (MAD)15.5
Skewness0.2241054885
Sum17004
Variance484.1370691
2020-08-25T01:25:28.885900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
844311.7%
 
52174.6%
 
32164.3%
 
47154.1%
 
33123.3%
 
27113.0%
 
17102.7%
 
42102.7%
 
3592.4%
 
3492.4%
 
4992.4%
 
2982.2%
 
5771.9%
 
3071.9%
 
7171.9%
 
2861.6%
 
3161.6%
 
6661.6%
 
1661.6%
 
2351.4%
 
6251.4%
 
1551.4%
 
5451.4%
 
4651.4%
 
4151.4%
 
Other values (60)12433.7%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
720.5%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
844311.7%
 
8310.3%
 
8210.3%
 
8110.3%
 
8010.3%
 
7910.3%
 
7810.3%
 
7720.5%
 
7620.5%
 
7510.3%
 

abdomen
Real number (ℝ≥0)

ZEROS

Distinct count6
Unique (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6032608695652173
Minimum0
Maximum5
Zeros31
Zeros (%)8.4%
Memory size3.0 KiB
2020-08-25T01:25:28.997578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.590225668
Coefficient of variation (CV)0.4413295972
Kurtosis0.01030280149
Mean3.60326087
Median Absolute Deviation (MAD)1
Skewness-1.068521262
Sum1326
Variance2.528817676
2020-08-25T01:25:29.101735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
514338.9%
 
49626.1%
 
35514.9%
 
0318.4%
 
1246.5%
 
2195.2%
 
ValueCountFrequency (%) 
0318.4%
 
1246.5%
 
2195.2%
 
35514.9%
 
49626.1%
 
514338.9%
 
ValueCountFrequency (%) 
514338.9%
 
49626.1%
 
35514.9%
 
2195.2%
 
1246.5%
 
0318.4%
 

rectal temperature
Real number (ℝ≥0)

Distinct count41
Unique (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.23641304347826
Minimum0
Maximum40
Zeros1
Zeros (%)0.3%
Memory size3.0 KiB
2020-08-25T01:25:29.219375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q116
median21
Q330
95-th percentile40
Maximum40
Range40
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.10037257
Coefficient of variation (CV)0.4346786465
Kurtosis-0.6709921366
Mean23.23641304
Median Absolute Deviation (MAD)5
Skewness0.4197525787
Sum8551
Variance102.0175261
2020-08-25T01:25:29.334490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
406918.8%
 
18349.2%
 
21236.2%
 
20236.2%
 
23215.7%
 
16215.7%
 
13164.3%
 
19154.1%
 
22143.8%
 
24143.8%
 
14102.7%
 
15102.7%
 
2592.4%
 
1792.4%
 
1071.9%
 
2861.6%
 
2661.6%
 
1161.6%
 
3051.4%
 
951.4%
 
3341.1%
 
3141.1%
 
841.1%
 
1241.1%
 
2741.1%
 
Other values (16)256.8%
 
ValueCountFrequency (%) 
010.3%
 
120.5%
 
210.3%
 
310.3%
 
420.5%
 
520.5%
 
610.3%
 
710.3%
 
841.1%
 
951.4%
 
ValueCountFrequency (%) 
406918.8%
 
3910.3%
 
3820.5%
 
3710.3%
 
3610.3%
 
3520.5%
 
3410.3%
 
3341.1%
 
3230.8%
 
3141.1%
 

pain
Real number (ℝ≥0)

ZEROS

Distinct count6
Unique (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4375
Minimum0
Maximum5
Zeros49
Zeros (%)13.3%
Memory size3.0 KiB
2020-08-25T01:25:29.476927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.664309186
Coefficient of variation (CV)0.6827935124
Kurtosis-1.190601841
Mean2.4375
Median Absolute Deviation (MAD)1
Skewness0.1865746523
Sum897
Variance2.769925068
2020-08-25T01:25:29.590869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
28222.3%
 
17720.9%
 
56317.1%
 
45013.6%
 
04913.3%
 
34712.8%
 
ValueCountFrequency (%) 
04913.3%
 
17720.9%
 
28222.3%
 
34712.8%
 
45013.6%
 
56317.1%
 
ValueCountFrequency (%) 
56317.1%
 
45013.6%
 
34712.8%
 
28222.3%
 
17720.9%
 
04913.3%
 

outcome
Categorical

Distinct count4
Unique (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
225
1
89
2
52
3
 
2
ValueCountFrequency (%) 
022561.1%
 
18924.2%
 
25214.1%
 
320.5%
 
2020-08-25T01:25:29.740748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
022561.1%
 
18924.2%
 
25214.1%
 
320.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
022561.1%
 
18924.2%
 
25214.1%
 
320.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
022561.1%
 
18924.2%
 
25214.1%
 
320.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
022561.1%
 
18924.2%
 
25214.1%
 
320.5%
 

mucous membranes
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3777173913043477
Minimum0
Maximum6
Zeros98
Zeros (%)26.6%
Memory size3.0 KiB
2020-08-25T01:25:29.862469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.043463216
Coefficient of variation (CV)0.8594222439
Kurtosis-0.9727435204
Mean2.377717391
Median Absolute Deviation (MAD)2
Skewness0.4631812768
Sum875
Variance4.175741914
2020-08-25T01:25:29.971257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
09826.6%
 
28122.0%
 
35013.6%
 
64813.0%
 
13810.3%
 
4287.6%
 
5256.8%
 
ValueCountFrequency (%) 
09826.6%
 
13810.3%
 
28122.0%
 
35013.6%
 
4287.6%
 
5256.8%
 
64813.0%
 
ValueCountFrequency (%) 
64813.0%
 
5256.8%
 
4287.6%
 
35013.6%
 
28122.0%
 
13810.3%
 
09826.6%
 

peristalsis
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.203804347826087
Minimum0
Maximum4
Zeros49
Zeros (%)13.3%
Memory size3.0 KiB
2020-08-25T01:25:30.081967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.169217668
Coefficient of variation (CV)0.5305451319
Kurtosis-0.3865068356
Mean2.203804348
Median Absolute Deviation (MAD)1
Skewness-0.371718575
Sum811
Variance1.367069956
2020-08-25T01:25:30.202510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
215441.8%
 
39124.7%
 
45214.1%
 
04913.3%
 
1226.0%
 
ValueCountFrequency (%) 
04913.3%
 
1226.0%
 
215441.8%
 
39124.7%
 
45214.1%
 
ValueCountFrequency (%) 
45214.1%
 
39124.7%
 
215441.8%
 
1226.0%
 
04913.3%
 

target
Categorical

Distinct count2
Unique (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
1
232
2
136
ValueCountFrequency (%) 
123263.0%
 
213637.0%
 
2020-08-25T01:25:30.350850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
123263.0%
 
213637.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
123263.0%
 
213637.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
123263.0%
 
213637.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
123263.0%
 
213637.0%
 

Interactions

2020-08-25T01:24:59.671101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:24:59.826331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:24:59.985486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:00.126675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:00.282203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:00.424327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:00.782319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:00.928491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:01.071051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:01.213995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:01.372286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:01.516433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:01.674193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:01.829429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:01.996334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:02.158515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:02.303913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:02.474273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:02.625360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:02.781796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:02.934245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:03.092774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:03.240311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:03.397334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:03.543140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:03.694749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:03.871336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:04.009721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:04.152870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:04.276109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:04.413738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:04.540544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:04.675412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:04.799832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:04.920217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:05.044441image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:05.392099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:05.516736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:05.653177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:05.787411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:05.943079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:06.116247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:06.263397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:06.428289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:06.579103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:06.735730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:06.887323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:07.036699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:07.186082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:07.357043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:07.501104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:07.662291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:07.819666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:07.959551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:08.103092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:08.256608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:08.402016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:08.526785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:08.663962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:08.789574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:08.911219image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:09.034546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:09.171536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:09.294067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:09.425087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:09.572349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:09.938605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:10.097605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:10.246921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:10.416777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:10.572427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:10.726434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:10.877860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:11.033250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:11.197440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:11.367141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:11.509650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:11.657923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:11.821425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:11.980500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:12.123117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:12.254873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:12.400814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:12.530741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:12.686889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:12.829139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:12.971068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:13.102494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:13.249877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:13.375760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:13.506917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:13.647142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:13.783823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:13.923046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:14.042861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:14.179435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:14.522552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:14.656463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:14.781859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:14.898689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:15.018309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:15.150662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:15.278166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:15.416197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:15.553685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:15.695389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:15.849902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:15.981996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:16.121336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:16.254275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:16.396454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:16.537374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:16.671006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:16.810065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:16.957016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:17.083727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:17.214363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:17.360048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:17.516095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:17.674291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:17.820208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:17.986428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:18.128256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:18.308402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:18.467448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:18.605850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:18.747255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:19.123581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:19.264088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:19.413811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:19.574485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:19.725626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:19.874953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:20.009727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:20.154293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:20.280024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:20.422543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:20.552091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:20.673815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:20.798542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:20.935547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:21.060272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:21.190206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:21.328103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:21.475927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:21.631700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:21.763201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:21.909650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:22.039807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:22.183464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:22.325958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:22.456912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:22.594003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:22.738286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:22.868417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:23.005224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:23.148586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:23.311834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:23.699474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:23.849854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:24.012748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:24.157525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:24.311836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:24.461944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:24.600283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:24.738638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:24.900878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:25.039077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:25.195417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:25:30.496121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:25:30.847802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:25:31.196490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:25:31.727672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-25T01:25:32.057044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-25T01:25:25.532293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:26.041192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

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910092013050401812952140022

Last rows

surgerynasogastric refluxcapillary refill timeAgerectal examinationtemperature of extremitiesabdomcentesis total proteinabdominal distensionpulserespiratory ratepacked cell volumenasogastric tubetotal proteinabdomenrectal temperaturepainoutcomemucous membranesperistalsistarget
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36413012044024201232921600022
3650001001103512204231812101
3661301424402411331601800202
36712014244149192202751421021

Duplicate rows

Most frequent

surgerynasogastric refluxcapillary refill timeAgerectal examinationtemperature of extremitiesabdomcentesis total proteinabdominal distensionpulserespiratory ratepacked cell volumenasogastric tubetotal proteinabdomenrectal temperaturepainoutcomemucous membranesperistalsistargetcount
0001123262411521147421202212
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8100103440281526072013502122
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