selfplot

selfplot is a python package for visualizing numerical data for Exploratory data analysis and Model Diagnostics.

During statistical analysis of data, a data scientist often needs to build various kinds of plots to explore or confirm a hypothesis regarding the data. It can sometimes be a hurdle to write multiple lines of code before actually seeing a plot. In such cases, it is practical to minimize the time delay between the conception of the hypothesis and the visualization of data.

The following python package is an attempt to minimize the time taken to make visualizations. Reasonable defaults are set for each plot; however, they can be changed manually if required. All plots can be made with just one line of code. The package is based on matplotlib and seaborn plotting functions. Visualizations are shown on the boston housing dataset.

Description

Following is a list and a brief description of the plots implemented in selfplot. For Examples of the plots on data please view 'selfplot Examples.html':

Exploratory Data Analysis visualizations

univariate() - Plot a histogram and a boxplot of data. Can be used to detect skew in data and the corresponding number of outliers through the boxplot.
ts_univariate() - Plot a time series plot along with a violin plot to visualize the distribution.
bivariate_binning() - Plot a variable y as a function of bins of variable x and plot the boxplot of y for every bin in x. This is similar to a scatterplot except that one of the variables is binned. The bins of x can be user defined.
histogram() - Create histogram of data
line() - Create Line plot or time series plot
kde() - Plot the kernel density estimate
box() - Plot the boxplot of a dataframe
violin() - Plot a violin plot of a dataframe
scatter() - Plot a scatter plot
hexbin() - Plot a hexbin plot
heatmap() - Plot a heatmap. This can be useful for visualizing correlations.
bar() - Make a bar plot with annotation

Model Analysis Plots

residual_diagnose() - Visualize distribution of Residuals of a Regression Model via parity and density plots; visualize behaviour against predicted and actual values.
prediction_diagnose() - Compare distributions of Predicted value and True value. Also plots a time series graph of predicted and true values.

How to use

To use the package, copy selfplot.py to the Lib\site-packages folder in the python installation folder.
If you have Anaconda installed, copy selfplot.py to Anaconda\Lib\site-packages\

After copying the file, the package can be imported in jupyter notebook/Spyder:

Exploratory Data Analysis visualization using selfplot

import selfplot    

# Univariate plot - Can be used to visualize skew in data and detect outliers.
# The histogram and the boxplot are positioned to make it easy to see which points on the histogram are outliers.
selfplot.univariate(X)

# Univariate time series plot - Time series plot of data and visualization of its distribution using Violin plot
selfplot.ts_univariate(ts_median_housing_prices)

# Bivariate Binning plots - Can be used to visualize one variable as a function of another.
# The variable on x axis is divided into user-defined bins. 
# The boxplot shows the distribution of the y variable in each bin of the x variable
selfplot.bivariate_binning(data = data, x = 'LSTAT', y = 'median_housing_price', bins = range(1, 30, 5))

# Scatterplot with reasonable defaults
selfplot.scatter(data['LSTAT'], data['RM'], c = data['median_housing_price'])

Model Analysis using selfplot

# Model Prediction Analysis Plots
selfplot.prediction_diagnose(y_true = y_true, y_pred = y_pred)

# Residual Analysis Plots
selfplot.residual_diagnose(y_true = y_true, y_pred = y_pred)

For other examples of using selfplot on data please view 'selfplot Examples.html'.