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
CAT5
BOOL2

Reproduction

Analysis started2020-08-25 01:18:12.667401
Analysis finished2020-08-25 01:18:38.765757
Duration26.1 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 128 (34.8%) zeros Zeros
temp_extremities has 65 (17.7%) zeros Zeros
abdominal_distension has 65 (17.7%) zeros Zeros
pulse has 13 (3.5%) zeros Zeros
respiratory_rate has 4 (1.1%) zeros Zeros
abdomen has 143 (38.9%) zeros Zeros
pain has 63 (17.1%) zeros Zeros
mucous_membranes has 48 (13.0%) zeros Zeros
peristalsis has 52 (14.1%) zeros Zeros

Variables

surgery
Categorical

Distinct count3
Unique (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2
214
1
152
0
 
2
ValueCountFrequency (%) 
221458.2%
 
115241.3%
 
020.5%
 
2020-08-25T01:18:38.842644image/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 (%) 
221458.2%
 
115241.3%
 
020.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
221458.2%
 
115241.3%
 
020.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
221458.2%
 
115241.3%
 
020.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
221458.2%
 
115241.3%
 
020.5%
 
Distinct count4
Unique (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
3
141
2
133
1
49
0
45
ValueCountFrequency (%) 
314138.3%
 
213336.1%
 
14913.3%
 
04512.2%
 
2020-08-25T01:18:38.978940image/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 (%) 
314138.3%
 
213336.1%
 
14913.3%
 
04512.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
314138.3%
 
213336.1%
 
14913.3%
 
04512.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
314138.3%
 
213336.1%
 
14913.3%
 
04512.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
314138.3%
 
213336.1%
 
14913.3%
 
04512.2%
 
Distinct count4
Unique (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
1
232
0
96
2
 
38
3
 
2
ValueCountFrequency (%) 
123263.0%
 
09626.1%
 
23810.3%
 
320.5%
 
2020-08-25T01:18:39.116926image/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 (%) 
123263.0%
 
09626.1%
 
23810.3%
 
320.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
123263.0%
 
09626.1%
 
23810.3%
 
320.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
123263.0%
 
09626.1%
 
23810.3%
 
320.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
123263.0%
 
09626.1%
 
23810.3%
 
320.5%
 

Age
Boolean

Distinct count2
Unique (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
340
1
 
28
ValueCountFrequency (%) 
034092.4%
 
1287.6%
 

rectal_examination
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4483695652173914
Minimum0
Maximum4
Zeros128
Zeros (%)34.8%
Memory size3.0 KiB
2020-08-25T01:18:39.231876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.460673412
Coefficient of variation (CV)1.008494964
Kurtosis-0.8728684877
Mean1.448369565
Median Absolute Deviation (MAD)1
Skewness0.6985231946
Sum533
Variance2.133566817
2020-08-25T01:18:39.341867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
012834.8%
 
19726.4%
 
46818.5%
 
26116.6%
 
3143.8%
 
ValueCountFrequency (%) 
012834.8%
 
19726.4%
 
26116.6%
 
3143.8%
 
46818.5%
 
ValueCountFrequency (%) 
46818.5%
 
3143.8%
 
26116.6%
 
19726.4%
 
012834.8%
 

temp_extremities
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0244565217391304
Minimum0
Maximum4
Zeros65
Zeros (%)17.7%
Memory size3.0 KiB
2020-08-25T01:18:39.463223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.218365246
Coefficient of variation (CV)0.6018233699
Kurtosis-0.7504029798
Mean2.024456522
Median Absolute Deviation (MAD)1
Skewness-0.2832391007
Sum745
Variance1.484413873
2020-08-25T01:18:39.579634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
213536.7%
 
39525.8%
 
06517.7%
 
43910.6%
 
1349.2%
 
ValueCountFrequency (%) 
06517.7%
 
1349.2%
 
213536.7%
 
39525.8%
 
43910.6%
 
ValueCountFrequency (%) 
43910.6%
 
39525.8%
 
213536.7%
 
1349.2%
 
06517.7%
 

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
2020-08-25T01:18:39.712228image/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
2020-08-25T01:18:39.808429image/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%
Mean1.9375
Minimum0
Maximum4
Zeros65
Zeros (%)17.7%
Memory size3.0 KiB
2020-08-25T01:18:39.911342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.366754444
Coefficient of variation (CV)0.7054216487
Kurtosis-1.125814796
Mean1.9375
Median Absolute Deviation (MAD)1
Skewness0.1777360651
Sum713
Variance1.868017711
2020-08-25T01:18:40.016313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
210127.4%
 
18523.1%
 
47520.4%
 
06517.7%
 
34211.4%
 
ValueCountFrequency (%) 
06517.7%
 
18523.1%
 
210127.4%
 
34211.4%
 
47520.4%
 
ValueCountFrequency (%) 
47520.4%
 
34211.4%
 
210127.4%
 
18523.1%
 
06517.7%
 

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
2020-08-25T01:18:40.133111image/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
2020-08-25T01:18:40.237792image/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
2020-08-25T01:18:40.349411image/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
2020-08-25T01:18:40.472726image/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
2020-08-25T01:18:40.596150image/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
2020-08-25T01:18:40.698066image/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
0
131
3
121
1
89
2
27
ValueCountFrequency (%) 
013135.6%
 
312132.9%
 
18924.2%
 
2277.3%
 
2020-08-25T01:18:40.836488image/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 (%) 
013135.6%
 
312132.9%
 
18924.2%
 
2277.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
013135.6%
 
312132.9%
 
18924.2%
 
2277.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
013135.6%
 
312132.9%
 
18924.2%
 
2277.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
013135.6%
 
312132.9%
 
18924.2%
 
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
2020-08-25T01:18:40.943808image/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
2020-08-25T01:18:41.048368image/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%
Mean1.377717391304348
Minimum0
Maximum5
Zeros143
Zeros (%)38.9%
Memory size3.0 KiB
2020-08-25T01:18:41.338985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.552441413
Coefficient of variation (CV)1.12682138
Kurtosis-0.04221692859
Mean1.377717391
Median Absolute Deviation (MAD)1
Skewness1.036049217
Sum507
Variance2.41007434
2020-08-25T01:18:41.443934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
014338.9%
 
19626.1%
 
25514.9%
 
4318.4%
 
5246.5%
 
3195.2%
 
ValueCountFrequency (%) 
014338.9%
 
19626.1%
 
25514.9%
 
3195.2%
 
4318.4%
 
5246.5%
 
ValueCountFrequency (%) 
5246.5%
 
4318.4%
 
3195.2%
 
25514.9%
 
19626.1%
 
014338.9%
 

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
2020-08-25T01:18:41.555136image/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
2020-08-25T01:18:41.667450image/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%
Mean2.5625
Minimum0
Maximum5
Zeros63
Zeros (%)17.1%
Memory size3.0 KiB
2020-08-25T01:18:41.801156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.661031573
Coefficient of variation (CV)0.648207443
Kurtosis-1.196128504
Mean2.5625
Median Absolute Deviation (MAD)1
Skewness-0.1944067246
Sum943
Variance2.759025886
2020-08-25T01:18:41.903778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
48222.3%
 
37720.9%
 
06317.1%
 
25013.6%
 
14913.3%
 
54712.8%
 
ValueCountFrequency (%) 
06317.1%
 
14913.3%
 
25013.6%
 
37720.9%
 
48222.3%
 
54712.8%
 
ValueCountFrequency (%) 
54712.8%
 
48222.3%
 
37720.9%
 
25013.6%
 
14913.3%
 
06317.1%
 

outcome
Categorical

Distinct count4
Unique (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
3
225
1
89
2
52
0
 
2
ValueCountFrequency (%) 
322561.1%
 
18924.2%
 
25214.1%
 
020.5%
 
2020-08-25T01:18:42.039328image/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 (%) 
322561.1%
 
18924.2%
 
25214.1%
 
020.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number368100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
322561.1%
 
18924.2%
 
25214.1%
 
020.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common368100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
322561.1%
 
18924.2%
 
25214.1%
 
020.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
322561.1%
 
18924.2%
 
25214.1%
 
020.5%
 

mucous_membranes
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5244565217391304
Minimum0
Maximum6
Zeros48
Zeros (%)13.0%
Memory size3.0 KiB
2020-08-25T01:18:42.149355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.046982155
Coefficient of variation (CV)0.5807937032
Kurtosis-1.070598147
Mean3.524456522
Median Absolute Deviation (MAD)2
Skewness-0.452161328
Sum1297
Variance4.190135944
2020-08-25T01:18:42.259362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
49826.6%
 
68122.0%
 
55013.6%
 
04813.0%
 
13810.3%
 
2287.6%
 
3256.8%
 
ValueCountFrequency (%) 
04813.0%
 
13810.3%
 
2287.6%
 
3256.8%
 
49826.6%
 
55013.6%
 
68122.0%
 
ValueCountFrequency (%) 
68122.0%
 
55013.6%
 
49826.6%
 
3256.8%
 
2287.6%
 
13810.3%
 
04813.0%
 

peristalsis
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0081521739130435
Minimum0
Maximum4
Zeros52
Zeros (%)14.1%
Memory size3.0 KiB
2020-08-25T01:18:42.370912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.214103703
Coefficient of variation (CV)0.6045875004
Kurtosis-1.195488635
Mean2.008152174
Median Absolute Deviation (MAD)1
Skewness-0.291214938
Sum739
Variance1.474047802
2020-08-25T01:18:42.482630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
315441.8%
 
19124.7%
 
05214.1%
 
24913.3%
 
4226.0%
 
ValueCountFrequency (%) 
05214.1%
 
19124.7%
 
24913.3%
 
315441.8%
 
4226.0%
 
ValueCountFrequency (%) 
4226.0%
 
315441.8%
 
24913.3%
 
19124.7%
 
05214.1%
 

target
Boolean

Distinct count2
Unique (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
1
232
0
136
ValueCountFrequency (%) 
123263.0%
 
013637.0%
 

Interactions

2020-08-25T01:18:13.756534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:13.912555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:14.062976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:14.212127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:14.367798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:14.506275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:14.654300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:14.796147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:14.932300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:15.068475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:15.217315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:15.368881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:15.513000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:15.664120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:15.824380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:15.976716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:16.286862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:16.438283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:16.573882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:16.718858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:16.861363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:17.000590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:17.137681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:17.290549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:17.426996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:17.570071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:17.719122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:17.854296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:17.984884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:18.102796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:18.232615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:18.352950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:18.481442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:18.608192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:18.723073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:18.843527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:18.980006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:19.097976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:19.216936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:19.345141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:19.497996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:19.649057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:19.788810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:19.942801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:20.081599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:20.247452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:20.576592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:20.713917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:20.851304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:21.001033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:21.136231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:21.277135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:21.428113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:21.562305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:21.695148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:21.822550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:21.958012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:22.079062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:22.210608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:22.330624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:22.453986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:22.575972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:22.715927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:22.834237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:22.962215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:23.094507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:23.244509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:23.393585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:23.536320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:23.691411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:23.833690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:23.984400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:24.123549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:24.257650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:24.392902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:24.546835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:24.862089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:25.006032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:25.165063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:25.301059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:25.436488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:25.565579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:25.700724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:25.822361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:25.955133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:26.080657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:26.202306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:26.324887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:26.459432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:26.583437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:26.714407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:26.851346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:26.984720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:27.117111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:27.234375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:27.362590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:27.479447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:27.608672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:27.727022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:27.842641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:27.961826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:28.094971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:28.210644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:28.341048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:28.475807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:28.616100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:28.750795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:29.060922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:29.196857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:29.320702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:29.456820image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:29.579990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:29.696564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:29.816119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:29.950097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:30.072697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:30.199028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:30.330472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:30.483660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:30.636787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:30.773528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:30.924883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:31.061158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:31.218760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:31.364045image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:31.501037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:31.649380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:31.796521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:31.931512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:32.072302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:32.226224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:32.374247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:32.511983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:32.633536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:32.781730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:32.904243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:33.042715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:33.354510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:33.472419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:33.590958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:33.733355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:33.853453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:33.979406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:34.112375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:34.258913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:34.402994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:34.531874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:34.674304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:34.804323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:34.944451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:35.073015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:35.198005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:35.323934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:35.464566image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:35.587748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:35.717928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:35.855217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:36.006694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:36.153951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:36.291271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:36.443026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:36.583698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:36.736669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:36.876202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:37.012766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:37.147728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:37.303230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:37.627294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:37.773793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:18:42.641196image/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.
2020-08-25T01:18:42.993197image/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.
2020-08-25T01:18:43.345958image/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.
2020-08-25T01:18:43.701221image/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.
2020-08-25T01:18:44.009666image/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

2020-08-25T01:18:38.071247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:38.583839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

surgerynasogastric_refluxcapillary_refill_timeAgerectal_examinationtemp_extremitiesabdomcentesis_total_proteinabdominal_distensionpulserespiratory_ratepacked_cell_volumenasogastric_tubetotal_proteinabdomenrectal_temperaturepainoutcomemucous_membranesperistalsistarget
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913112313250401832902123430

Last rows

surgerynasogastric_refluxcapillary_refill_timeAgerectal_examinationtemp_extremitiesabdomcentesis_total_proteinabdominal_distensionpulserespiratory_ratepacked_cell_volumenasogastric_tubetotal_proteinabdomenrectal_temperaturepainoutcomemucous_membranesperistalsistarget
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3652310431123512214221832121
3661210024422411301641813620
36711100244449192212701441431

Duplicate rows

Most frequent

surgerynasogastric_refluxcapillary_refill_timeAgerectal_examinationtemp_extremitiesabdomcentesis_total_proteinabdominal_distensionpulserespiratory_ratepacked_cell_volumenasogastric_tubetotal_proteinabdomenrectal_temperaturepainoutcomemucous_membranesperistalsistargetcount
0111002444491922127014414312
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2131041442281526172413036402
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