Dataset statistics
| Number of variables | 7 |
|---|---|
| Number of observations | 160 |
| Missing cells | 0 |
| Missing cells (%) | 0.0% |
| Duplicate rows | 96 |
| Duplicate rows (%) | 60.0% |
| Total size in memory | 8.9 KiB |
| Average record size in memory | 56.8 B |
Variable types
| BOOL | 7 |
|---|
Reproduction
| Analysis started | 2020-08-25 01:20:30.346916 |
|---|---|
| Analysis finished | 2020-08-25 01:20:30.968090 |
| Duration | 0.62 seconds |
| Version | pandas-profiling v2.8.0 |
| Command line | pandas_profiling --config_file config.yaml [YOUR_FILE.csv] |
| Download configuration | config.yaml |
| Dataset has 96 (60.0%) duplicate rows | Duplicates |
A0
Boolean
| Distinct count | 2 |
|---|---|
| Unique (%) | 1.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.4 KiB |
| 1 | |
|---|---|
| 0 |
| Value | Count | Frequency (%) | |
| 1 | 80 | 50.0% | |
| 0 | 80 | 50.0% |
A1
Boolean
| Distinct count | 2 |
|---|---|
| Unique (%) | 1.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.4 KiB |
| 1 | |
|---|---|
| 0 |
| Value | Count | Frequency (%) | |
| 1 | 80 | 50.0% | |
| 0 | 80 | 50.0% |
B0
Boolean
| Distinct count | 2 |
|---|---|
| Unique (%) | 1.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.4 KiB |
| 1 | |
|---|---|
| 0 |
| Value | Count | Frequency (%) | |
| 1 | 80 | 50.0% | |
| 0 | 80 | 50.0% |
B1
Boolean
| Distinct count | 2 |
|---|---|
| Unique (%) | 1.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.4 KiB |
| 1 | |
|---|---|
| 0 |
| Value | Count | Frequency (%) | |
| 1 | 80 | 50.0% | |
| 0 | 80 | 50.0% |
Irrelevant
Boolean
| Distinct count | 2 |
|---|---|
| Unique (%) | 1.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.4 KiB |
| 1 | |
|---|---|
| 0 |
| Value | Count | Frequency (%) | |
| 1 | 80 | 50.0% | |
| 0 | 80 | 50.0% |
Correlated
Boolean
| Distinct count | 2 |
|---|---|
| Unique (%) | 1.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.4 KiB |
| 0 | |
|---|---|
| 1 |
| Value | Count | Frequency (%) | |
| 0 | 86 | 53.8% | |
| 1 | 74 | 46.2% |
target
Boolean
| Distinct count | 2 |
|---|---|
| Unique (%) | 1.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.4 KiB |
| 0 | |
|---|---|
| 1 |
| Value | Count | Frequency (%) | |
| 0 | 90 | 56.2% | |
| 1 | 70 | 43.8% |
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.
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.
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.
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.First rows
| A0 | A1 | B0 | B1 | Irrelevant | Correlated | target | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 4 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
| 5 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 6 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 7 | 0 | 0 | 1 | 1 | 1 | 1 | 1 |
| 8 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
| 9 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
Last rows
| A0 | A1 | B0 | B1 | Irrelevant | Correlated | target | |
|---|---|---|---|---|---|---|---|
| 150 | 1 | 1 | 0 | 1 | 1 | 0 | 1 |
| 151 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| 152 | 1 | 1 | 1 | 0 | 0 | 0 | 1 |
| 153 | 1 | 1 | 1 | 0 | 0 | 1 | 1 |
| 154 | 1 | 1 | 1 | 0 | 1 | 0 | 1 |
| 155 | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
| 156 | 1 | 1 | 1 | 1 | 0 | 0 | 1 |
| 157 | 1 | 1 | 1 | 1 | 0 | 1 | 1 |
| 158 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
| 159 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Most frequent
| A0 | A1 | B0 | B1 | Irrelevant | Correlated | target | count | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 |
| 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
| 3 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 4 |
| 6 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 4 |
| 9 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 4 |
| 12 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 4 |
| 15 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 4 |
| 16 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 4 |
| 17 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 4 |