Overview

Dataset statistics

Number of variables7
Number of observations1728
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.6 KiB
Average record size in memory56.1 B

Variable types

CAT7

Reproduction

Analysis started2020-08-25 01:12:48.716803
Analysis finished2020-08-25 01:12:49.630741
Duration0.91 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

buying is uniformly distributed Uniform
maint is uniformly distributed Uniform
doors is uniformly distributed Uniform
persons is uniformly distributed Uniform
lug_boot is uniformly distributed Uniform
safety is uniformly distributed Uniform

Variables

buying
Categorical

UNIFORM

Distinct count4
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
3
432
2
432
1
432
0
432
ValueCountFrequency (%) 
343225.0%
 
243225.0%
 
143225.0%
 
043225.0%
 
2020-08-25T01:12:49.707330image/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 (%) 
343225.0%
 
043225.0%
 
243225.0%
 
143225.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1728100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
343225.0%
 
043225.0%
 
243225.0%
 
143225.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1728100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
343225.0%
 
043225.0%
 
243225.0%
 
143225.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1728100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
343225.0%
 
043225.0%
 
243225.0%
 
143225.0%
 

maint
Categorical

UNIFORM

Distinct count4
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
3
432
2
432
1
432
0
432
ValueCountFrequency (%) 
343225.0%
 
243225.0%
 
143225.0%
 
043225.0%
 
2020-08-25T01:12:50.033216image/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 (%) 
343225.0%
 
043225.0%
 
243225.0%
 
143225.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1728100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
343225.0%
 
043225.0%
 
243225.0%
 
143225.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1728100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
343225.0%
 
043225.0%
 
243225.0%
 
143225.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1728100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
343225.0%
 
043225.0%
 
243225.0%
 
143225.0%
 

doors
Categorical

UNIFORM

Distinct count4
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
3
432
2
432
1
432
0
432
ValueCountFrequency (%) 
343225.0%
 
243225.0%
 
143225.0%
 
043225.0%
 
2020-08-25T01:12:50.185494image/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 (%) 
043225.0%
 
143225.0%
 
243225.0%
 
343225.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1728100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
043225.0%
 
143225.0%
 
243225.0%
 
343225.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1728100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
043225.0%
 
143225.0%
 
243225.0%
 
343225.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1728100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
043225.0%
 
143225.0%
 
243225.0%
 
343225.0%
 

persons
Categorical

UNIFORM

Distinct count3
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
2
576
1
576
0
576
ValueCountFrequency (%) 
257633.3%
 
157633.3%
 
057633.3%
 
2020-08-25T01:12:50.329258image/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 (%) 
057633.3%
 
157633.3%
 
257633.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1728100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
057633.3%
 
157633.3%
 
257633.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1728100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
057633.3%
 
157633.3%
 
257633.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1728100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
057633.3%
 
157633.3%
 
257633.3%
 

lug_boot
Categorical

UNIFORM

Distinct count3
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
2
576
1
576
0
576
ValueCountFrequency (%) 
257633.3%
 
157633.3%
 
057633.3%
 
2020-08-25T01:12:50.472311image/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 (%) 
257633.3%
 
157633.3%
 
057633.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1728100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
257633.3%
 
157633.3%
 
057633.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1728100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
257633.3%
 
157633.3%
 
057633.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1728100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
257633.3%
 
157633.3%
 
057633.3%
 

safety
Categorical

UNIFORM

Distinct count3
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
2
576
1
576
0
576
ValueCountFrequency (%) 
257633.3%
 
157633.3%
 
057633.3%
 
2020-08-25T01:12:50.616299image/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 (%) 
157633.3%
 
257633.3%
 
057633.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1728100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
157633.3%
 
257633.3%
 
057633.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1728100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
157633.3%
 
257633.3%
 
057633.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1728100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
157633.3%
 
257633.3%
 
057633.3%
 

target
Categorical

Distinct count4
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
2
1210
0
384
1
 
69
3
 
65
ValueCountFrequency (%) 
2121070.0%
 
038422.2%
 
1694.0%
 
3653.8%
 
2020-08-25T01:12:50.760478image/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 (%) 
2121070.0%
 
038422.2%
 
1694.0%
 
3653.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1728100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2121070.0%
 
038422.2%
 
1694.0%
 
3653.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1728100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
2121070.0%
 
038422.2%
 
1694.0%
 
3653.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1728100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
2121070.0%
 
038422.2%
 
1694.0%
 
3653.8%
 

Correlations

2020-08-25T01:12:50.877432image/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.
2020-08-25T01:12:51.069295image/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.
2020-08-25T01:12:51.267655image/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.
2020-08-25T01:12:51.464174image/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.
2020-08-25T01:12:51.644024image/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

2020-08-25T01:12:49.325702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:12:49.535277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

buyingmaintdoorspersonslug_bootsafetytarget
03300212
13300222
23300202
33300112
43300122
53300102
63300012
73300002
83301212
93301222

Last rows

buyingmaintdoorspersonslug_bootsafetytarget
17181112021
17191121103
17201122220
17211122201
17221122003
17231130112
17241130102
17251131121
17261131103
17271132012