Overview

Dataset statistics

Number of variables14
Number of observations294
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory32.3 KiB
Average record size in memory112.4 B

Variable types

CAT6
NUM5
BOOL3

Reproduction

Analysis started2020-08-25 01:25:44.373365
Analysis finished2020-08-25 01:25:49.196125
Duration4.82 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 1 (0.3%) duplicate rows Duplicates
trestbps has 6 (2.0%) zeros Zeros
thalach has 7 (2.4%) zeros Zeros
oldpeak has 189 (64.3%) zeros Zeros

Variables

age
Real number (ℝ≥0)

Distinct count38
Unique (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.826530612244895
Minimum28.0
Maximum66.0
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-08-25T01:25:49.239472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile34
Q142
median49
Q354
95-th percentile59
Maximum66
Range38
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.811812413
Coefficient of variation (CV)0.1633363807
Kurtosis-0.5025373592
Mean47.82653061
Median Absolute Deviation (MAD)5.5
Skewness-0.2842612387
Sum14061
Variance61.02441318
2020-08-25T01:25:49.350931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
54258.5%
 
48196.5%
 
52175.8%
 
49155.1%
 
55155.1%
 
46134.4%
 
53124.1%
 
43124.1%
 
50124.1%
 
39113.7%
 
41113.7%
 
47103.4%
 
56103.4%
 
5193.1%
 
5893.1%
 
3782.7%
 
4582.7%
 
5982.7%
 
3872.4%
 
4472.4%
 
4272.4%
 
4072.4%
 
5751.7%
 
3651.7%
 
3551.7%
 
Other values (13)279.2%
 
ValueCountFrequency (%) 
2810.3%
 
2920.7%
 
3010.3%
 
3120.7%
 
3241.4%
 
3320.7%
 
3441.4%
 
3551.7%
 
3651.7%
 
3782.7%
 
ValueCountFrequency (%) 
6610.3%
 
6531.0%
 
6310.3%
 
6220.7%
 
6120.7%
 
6020.7%
 
5982.7%
 
5893.1%
 
5751.7%
 
56103.4%
 

sex
Boolean

Distinct count2
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
1
213
0
81
ValueCountFrequency (%) 
121372.4%
 
08127.6%
 

cp
Categorical

Distinct count4
Unique (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
4
123
2
106
3
54
1
 
11
ValueCountFrequency (%) 
412341.8%
 
210636.1%
 
35418.4%
 
1113.7%
 
2020-08-25T01:25:49.492485image/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 (%) 
412341.8%
 
210636.1%
 
35418.4%
 
1113.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number294100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
412341.8%
 
210636.1%
 
35418.4%
 
1113.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common294100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
412341.8%
 
210636.1%
 
35418.4%
 
1113.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII294100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
412341.8%
 
210636.1%
 
35418.4%
 
1113.7%
 

trestbps
Real number (ℝ≥0)

ZEROS

Distinct count32
Unique (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.534013605442176
Minimum0
Maximum31
Zeros6
Zeros (%)2.0%
Memory size2.4 KiB
2020-08-25T01:25:49.600742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median14
Q319
95-th percentile25
Maximum31
Range31
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.799206906
Coefficient of variation (CV)0.4678134403
Kurtosis-0.8026760655
Mean14.53401361
Median Absolute Deviation (MAD)5
Skewness-0.01626405774
Sum4273
Variance46.22921455
2020-08-25T01:25:49.712907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
96522.1%
 
145418.4%
 
195017.0%
 
22237.8%
 
4217.1%
 
24206.8%
 
1282.7%
 
062.0%
 
2662.0%
 
1651.7%
 
2151.7%
 
2551.7%
 
531.0%
 
1020.7%
 
720.7%
 
820.7%
 
1120.7%
 
610.3%
 
310.3%
 
210.3%
 
110.3%
 
3110.3%
 
1310.3%
 
3010.3%
 
1710.3%
 
Other values (7)72.4%
 
ValueCountFrequency (%) 
062.0%
 
110.3%
 
210.3%
 
310.3%
 
4217.1%
 
531.0%
 
610.3%
 
720.7%
 
820.7%
 
96522.1%
 
ValueCountFrequency (%) 
3110.3%
 
3010.3%
 
2910.3%
 
2810.3%
 
2710.3%
 
2662.0%
 
2551.7%
 
24206.8%
 
2310.3%
 
22237.8%
 

chol
Real number (ℝ≥0)

Distinct count154
Unique (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.01360544217687
Minimum0
Maximum153
Zeros1
Zeros (%)0.3%
Memory size2.4 KiB
2020-08-25T01:25:49.826786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.65
Q141.25
median73
Q3111
95-th percentile153
Maximum153
Range153
Interquartile range (IQR)69.75

Descriptive statistics

Standard deviation44.17448902
Coefficient of variation (CV)0.573593312
Kurtosis-1.050714606
Mean77.01360544
Median Absolute Deviation (MAD)35.5
Skewness0.1907190324
Sum22642
Variance1951.38548
2020-08-25T01:25:49.949038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
153237.8%
 
7051.7%
 
9551.7%
 
5951.7%
 
4041.4%
 
5341.4%
 
2841.4%
 
6341.4%
 
4541.4%
 
6441.4%
 
8241.4%
 
8341.4%
 
4441.4%
 
11331.0%
 
7231.0%
 
11031.0%
 
10931.0%
 
10631.0%
 
11931.0%
 
5431.0%
 
8431.0%
 
5131.0%
 
8831.0%
 
4931.0%
 
4831.0%
 
Other values (129)18462.6%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
420.7%
 
510.3%
 
631.0%
 
710.3%
 
820.7%
 
910.3%
 
ValueCountFrequency (%) 
153237.8%
 
15210.3%
 
15110.3%
 
15010.3%
 
14910.3%
 
14810.3%
 
14710.3%
 
14610.3%
 
14510.3%
 
14410.3%
 

fbs
Categorical

Distinct count3
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
266
1
 
20
2
 
8
ValueCountFrequency (%) 
026690.5%
 
1206.8%
 
282.7%
 
2020-08-25T01:25:50.093744image/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 (%) 
026690.5%
 
1206.8%
 
282.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number294100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
026690.5%
 
1206.8%
 
282.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common294100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
026690.5%
 
1206.8%
 
282.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII294100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
026690.5%
 
1206.8%
 
282.7%
 

restecg
Categorical

Distinct count4
Unique (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
235
1
52
2
 
6
3
 
1
ValueCountFrequency (%) 
023579.9%
 
15217.7%
 
262.0%
 
310.3%
 
2020-08-25T01:25:50.233951image/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 (%) 
023579.9%
 
15217.7%
 
262.0%
 
310.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number294100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
023579.9%
 
15217.7%
 
262.0%
 
310.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common294100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
023579.9%
 
15217.7%
 
262.0%
 
310.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII294100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
023579.9%
 
15217.7%
 
262.0%
 
310.3%
 

thalach
Real number (ℝ≥0)

ZEROS

Distinct count72
Unique (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.775510204081634
Minimum0
Maximum71
Zeros7
Zeros (%)2.4%
Memory size2.4 KiB
2020-08-25T01:25:50.343150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q120
median31
Q344
95-th percentile65.35
Maximum71
Range71
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.63908007
Coefficient of variation (CV)0.5381786571
Kurtosis-0.7044583236
Mean32.7755102
Median Absolute Deviation (MAD)13
Skewness0.1664840386
Sum9636
Variance311.1371456
2020-08-25T01:25:50.453946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
37299.9%
 
30217.1%
 
22175.8%
 
51144.8%
 
44134.4%
 
13113.7%
 
693.1%
 
1782.7%
 
3182.7%
 
2572.4%
 
4172.4%
 
072.4%
 
962.0%
 
3462.0%
 
5762.0%
 
5462.0%
 
2862.0%
 
1151.7%
 
1551.7%
 
4741.4%
 
2741.4%
 
2441.4%
 
1641.4%
 
1041.4%
 
6941.4%
 
Other values (47)7926.9%
 
ValueCountFrequency (%) 
072.4%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
693.1%
 
731.0%
 
810.3%
 
962.0%
 
ValueCountFrequency (%) 
7110.3%
 
7020.7%
 
6941.4%
 
6831.0%
 
6720.7%
 
6631.0%
 
6510.3%
 
6410.3%
 
6310.3%
 
6210.3%
 

exang
Categorical

Distinct count3
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
204
1
89
2
 
1
ValueCountFrequency (%) 
020469.4%
 
18930.3%
 
210.3%
 
2020-08-25T01:25:50.590300image/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 (%) 
020469.4%
 
18930.3%
 
210.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number294100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
020469.4%
 
18930.3%
 
210.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common294100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
020469.4%
 
18930.3%
 
210.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII294100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
020469.4%
 
18930.3%
 
210.3%
 

oldpeak
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5860544217687075
Minimum0.0
Maximum5.0
Zeros189
Zeros (%)64.3%
Memory size2.4 KiB
2020-08-25T01:25:50.700771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9086479156
Coefficient of variation (CV)1.550449723
Kurtosis2.180743047
Mean0.5860544218
Median Absolute Deviation (MAD)0
Skewness1.54882442
Sum172.3
Variance0.8256410346
2020-08-25T01:25:50.803920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
018964.3%
 
14113.9%
 
23110.5%
 
1.5165.4%
 
393.1%
 
2.531.0%
 
0.520.7%
 
0.810.3%
 
410.3%
 
510.3%
 
ValueCountFrequency (%) 
018964.3%
 
0.520.7%
 
0.810.3%
 
14113.9%
 
1.5165.4%
 
23110.5%
 
2.531.0%
 
393.1%
 
410.3%
 
510.3%
 
ValueCountFrequency (%) 
510.3%
 
410.3%
 
393.1%
 
2.531.0%
 
23110.5%
 
1.5165.4%
 
14113.9%
 
0.810.3%
 
0.520.7%
 
018964.3%
 

slope
Categorical

Distinct count4
Unique (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
3
190
1
91
0
 
12
2
 
1
ValueCountFrequency (%) 
319064.6%
 
19131.0%
 
0124.1%
 
210.3%
 
2020-08-25T01:25:50.944104image/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 (%) 
319064.6%
 
19131.0%
 
0124.1%
 
210.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number294100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
319064.6%
 
19131.0%
 
0124.1%
 
210.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common294100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
319064.6%
 
19131.0%
 
0124.1%
 
210.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII294100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
319064.6%
 
19131.0%
 
0124.1%
 
210.3%
 

ca
Boolean

Distinct count2
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
1
291
0
 
3
ValueCountFrequency (%) 
129199.0%
 
031.0%
 

thal
Categorical

Distinct count4
Unique (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
3
266
2
 
11
1
 
10
0
 
7
ValueCountFrequency (%) 
326690.5%
 
2113.7%
 
1103.4%
 
072.4%
 
2020-08-25T01:25:51.082610image/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 (%) 
326690.5%
 
2113.7%
 
1103.4%
 
072.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number294100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
326690.5%
 
2113.7%
 
1103.4%
 
072.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common294100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
326690.5%
 
2113.7%
 
1103.4%
 
072.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII294100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
326690.5%
 
2113.7%
 
1103.4%
 
072.4%
 

target
Boolean

Distinct count2
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
188
1
106
ValueCountFrequency (%) 
018863.9%
 
110636.1%
 

Interactions

2020-08-25T01:25:45.061704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:45.198202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:45.340469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:45.480513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:45.626481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:45.755700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:45.883424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:46.008328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:46.140459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:46.276150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:46.404541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:46.551263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:46.688218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:46.833032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:46.980762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:47.113832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:47.247242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:47.381540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:47.524212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:47.659234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:47.788325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:48.101040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:48.221917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:48.355972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:48.485604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:25:51.216838image/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.
2020-08-25T01:25:51.477670image/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.
2020-08-25T01:25:51.930712image/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.
2020-08-25T01:25:52.187715image/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.
2020-08-25T01:25:52.416040image/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

2020-08-25T01:25:48.738343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:25:49.063479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
049.0131922005200.03130
159.01219105003700.03130
249.0021131004600.03130
341.014574003100.03130
454.0142829003112.01131
556.0149152003000.03130
646.014465013000.03100
754.012437003100.03130
834.011195005700.03131
953.014970001010.03131

Last rows

agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
28458.0131442013000.03111
28551.00424115003711.01131
28654.0122427012201.00130
28745.00226153005700.03130
28847.0141996101710.03130
28955.014195800610.51130
29047.00314153003402.01130
29146.014145100700.03131
29248.003927001700.03130
29353.013927003000.03130

Duplicate rows

Most frequent

agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltargetcount
049.0024153004400.031302