Dataset statistics
| Number of variables | 5 |
|---|---|
| Number of observations | 210 |
| Missing cells | 0 |
| Missing cells (%) | 0.0% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 8.3 KiB |
| Average record size in memory | 40.6 B |
Variable types
| Numeric | 1 |
|---|---|
| Categorical | 4 |
AGE is highly correlated with AMPUTATION | High correlation |
AMPUTATION is highly correlated with AGE | High correlation |
AMPUTATION is uniformly distributed | Uniform |
Reproduction
| Analysis started | 2021-11-16 20:48:41.142486 |
|---|---|
| Analysis finished | 2021-11-16 20:48:42.048926 |
| Duration | 0.91 seconds |
| Software version | pandas-profiling v3.1.0 |
| Download configuration | config.json |
| Distinct | 71 |
|---|---|
| Distinct (%) | 33.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 55.0952381 |
| Minimum | 4 |
|---|---|
| Maximum | 89 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.8 KiB |
Quantile statistics
| Minimum | 4 |
|---|---|
| 5-th percentile | 16.9 |
| Q1 | 47.25 |
| median | 59 |
| Q3 | 68 |
| 95-th percentile | 80 |
| Maximum | 89 |
| Range | 85 |
| Interquartile range (IQR) | 20.75 |
Descriptive statistics
| Standard deviation | 18.58024047 |
|---|---|
| Coefficient of variation (CV) | 0.3372385911 |
| Kurtosis | 0.4244667576 |
| Mean | 55.0952381 |
| Median Absolute Deviation (MAD) | 10 |
| Skewness | -0.8567484108 |
| Sum | 11570 |
| Variance | 345.2253361 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 61 | 10 | 4.8% |
| 62 | 9 | 4.3% |
| 69 | 8 | 3.8% |
| 60 | 8 | 3.8% |
| 52 | 7 | 3.3% |
| 54 | 7 | 3.3% |
| 56 | 7 | 3.3% |
| 73 | 6 | 2.9% |
| 50 | 6 | 2.9% |
| 65 | 6 | 2.9% |
| Other values (61) | 136 |
| Value | Count | Frequency (%) |
| 4 | 2 | |
| 5 | 2 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 1 | |
| 11 | 1 | |
| 12 | 1 | |
| 15 | 1 | |
| 16 | 1 | |
| 18 | 2 |
| Value | Count | Frequency (%) |
| 89 | 1 | 0.5% |
| 88 | 2 | 1.0% |
| 85 | 1 | 0.5% |
| 84 | 1 | 0.5% |
| 83 | 1 | 0.5% |
| 81 | 1 | 0.5% |
| 80 | 5 | |
| 79 | 2 | 1.0% |
| 78 | 2 | 1.0% |
| 77 | 3 |
GENDER
Categorical
| Distinct | 2 |
|---|---|
| Distinct (%) | 1.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.8 KiB |
| F | |
|---|---|
| M |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | M |
|---|---|
| 2nd row | M |
| 3rd row | F |
| 4th row | F |
| 5th row | F |
Common Values
| Value | Count | Frequency (%) |
| F | 112 | |
| M | 98 |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| f | 112 | |
| m | 98 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
RACE
Categorical
| Distinct | 5 |
|---|---|
| Distinct (%) | 2.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.8 KiB |
| Asian | |
|---|---|
| Black | |
| White | |
| Coloured | |
| Other |
Length
| Max length | 8 |
|---|---|
| Median length | 5 |
| Mean length | 5.628571429 |
| Min length | 5 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Black |
|---|---|
| 2nd row | Black |
| 3rd row | Asian |
| 4th row | Black |
| 5th row | White |
Common Values
| Value | Count | Frequency (%) |
| Asian | 89 | |
| Black | 57 | |
| White | 29 | 13.8% |
| Coloured | 25 | 11.9% |
| Other | 10 | 4.8% |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| asian | 89 | |
| black | 57 | |
| white | 29 | 13.8% |
| coloured | 25 | 11.9% |
| other | 10 | 4.8% |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
DIABETES_CLASS
Categorical
| Distinct | 2 |
|---|---|
| Distinct (%) | 1.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.8 KiB |
| Type 2 diabetes | |
|---|---|
| Type 1 diabetes |
Length
| Max length | 15 |
|---|---|
| Median length | 15 |
| Mean length | 15 |
| Min length | 15 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Type 2 diabetes |
|---|---|
| 2nd row | Type 2 diabetes |
| 3rd row | Type 2 diabetes |
| 4th row | Type 2 diabetes |
| 5th row | Type 2 diabetes |
Common Values
| Value | Count | Frequency (%) |
| Type 2 diabetes | 135 | |
| Type 1 diabetes | 75 |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| diabetes | 210 | |
| type | 210 | |
| 2 | 135 | |
| 1 | 75 | 11.9% |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 2 |
|---|---|
| Distinct (%) | 1.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.8 KiB |
| 0 | |
|---|---|
| 1 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 1 |
|---|---|
| 2nd row | 1 |
| 3rd row | 1 |
| 4th row | 1 |
| 5th row | 1 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 105 | |
| 1 | 105 |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| 1 | 105 | |
| 0 | 105 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
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.
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.
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.
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.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. A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
First rows
| AGE | GENDER | RACE | DIABETES_CLASS | AMPUTATION | |
|---|---|---|---|---|---|
| 0 | 50 | M | Black | Type 2 diabetes | 1 |
| 1 | 47 | M | Black | Type 2 diabetes | 1 |
| 2 | 76 | F | Asian | Type 2 diabetes | 1 |
| 3 | 57 | F | Black | Type 2 diabetes | 1 |
| 4 | 67 | F | White | Type 2 diabetes | 1 |
| 5 | 56 | F | White | Type 2 diabetes | 1 |
| 6 | 66 | F | Asian | Type 2 diabetes | 1 |
| 7 | 62 | F | Coloured | Type 1 diabetes | 1 |
| 8 | 65 | F | Black | Type 2 diabetes | 1 |
| 9 | 80 | F | Asian | Type 1 diabetes | 1 |
Last rows
| AGE | GENDER | RACE | DIABETES_CLASS | AMPUTATION | |
|---|---|---|---|---|---|
| 200 | 60 | M | Coloured | Type 2 diabetes | 0 |
| 201 | 69 | M | White | Type 2 diabetes | 0 |
| 202 | 73 | F | Other | Type 2 diabetes | 0 |
| 203 | 59 | F | Asian | Type 2 diabetes | 0 |
| 204 | 75 | F | Asian | Type 2 diabetes | 0 |
| 205 | 48 | F | Coloured | Type 1 diabetes | 0 |
| 206 | 50 | M | Coloured | Type 2 diabetes | 0 |
| 207 | 19 | F | White | Type 1 diabetes | 0 |
| 208 | 88 | F | Black | Type 2 diabetes | 0 |
| 209 | 65 | F | Other | Type 2 diabetes | 0 |