Easy Graphing

A basic wrapper for Seaborn that has functions to create graphs and control their appearance.

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Motivation

When performing EDA in a Jupyter notebook, I found myself making the same types of graphs over and over. I wondered how I could make it easier for myself to plot these graphs and how I could make the functions more dynamic.

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Dependencies

For these functions to run properly, you should have the following installed:

• Seaborn
• Matplotlib.pyplot
• Numpy
• Scikit-learn
• Statsmodels
• Plotly
• Mlxtend

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graphs Contents

There are two sets of graphs included in the .py file: graphs for EDA and graphs for model evaluation.

For EDA:

Plot Type	Numeric Data	Categorical Data	Description
Bar Plots		✓	Takes a numeric variable and plots the corresponding measure of central tendency for a categorical column
Box Plots	✓	✓	Plots a box & whiskers plot for a numeric variable
Count Plots	✓	✓	Counts how many times a category appears in a categorical variable
Heat Maps	✓		Creates a heat map of numeric variable correlations
Histograms	✓		Plots histograms for a numeric variable
2D Histograms	✓		Plots two continuous variables (best for large datasets)
KDE Plots	✓		Plots a kernel density estimate for a numeric variable
Scatter Plots	✓		Plots two numeric variables against each other
Violin Plots	✓	✓	Plots a density estimate on top of a box plot

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plotly_graphs Contents

For EDA:

Plot Type	Numeric Data	Categorical Data	Description
2D Histogram	✓		Plots two numeric variables (best for large datasets)
Bar Chart		✓	Plots the frequency of a categorical variable
Box plot	✓		Plots a box & whiskers plot for a numeric variable
Double Histogram	✓		Plots 2 stacked or overlain histograms
Heatmap	✓		Plots a heatmap for numeric data
Histogram	✓		Plots a histogram for a numeric variable
Scatter Plot	✓		Plots two numeric variables against each other
Violin Plot	✓		Plots a violin plot for a numeric variable

Utilities:

Plot Type	Numeric Data	Categorical Data	Description
Table	✓	✓	Creates a table for whatever data is inputted; best for distribution

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model_evaluation Contents

Graphs

Plot Type	Numeric Data	Categorical Data	Description
Precision-Recall Curve		✓	Plots a precision-recall curve from a model's predicted probabilities
ROC Curve		✓	Plots a ROC curve from a model's predicted probabilities
Residual Plots	✓		Plots the residuals from a single or multiple model's probabilities

Metrics

Metric	Classification	Regression	Description
Adjusted R2		✓	The R2 score adjusted for the number of features in X
Negative Predictive Value	✓		Percentage of correctly predicted negatives out of all negatives
Specificity	✓		Percentage of correctly predicted negative predictions

Summaries

Summary	Description
Classification Summary	Calculates the recall, specificity, Matthews correlation coefficient, and AUROC score in a dataframe
Confusion Matrix Dataframe	Converts scikit-learn's confusion_matrix into a dataframe
Regression Summary	Calculates the RMSE, MAE, R2, & Adjusted2 in a dataframe
Scaled Regression Summary	Calculates the RMSE, MAE, & R2 for scaled data in a dataframe

stat_functions Contents

Statistic	Data Type	Description
χ2	Categorical-Categorical	A dataframe with the χ2 stats., p-values, & degrees of freedom for 2 variables
McNemar's Test	Classifier - Classifier	A dataframe with McNemar's statistic, p-value, & interpretation of the p-value for 2 classifiers
McNemar's Dataframe	Classifier-Classifier	A dataframe containing a contingency table for the McNemar's test
Pearson's r	Numeric-Numeric	A dataframe containing the Pearson's r coefs., p-values, & interpretation for numeric variables
Point Biserial r	Binary-Numeric	A dataframe with the point-biserial r coefs., p-values, & interpreation for numeric & binary variables
Cross-Validated F-Test	Regressor - Regressor	A datafrmae with the 5X2 cross-validated F-score, p-value, & interpretation for two regressors

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Contributions

Pull requests are welcome, but for any major issues please open an issue first to discuss what needs to be changed.

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Road Map

• Bokeh interactive functions
• Statistical tests for EDA and model evaluation

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Project Status

Work is ongoing: I add or modify code as needed.

I am focusing on statistical functions at the moment, but I am open to adding more visualization functions as well.

Latest Updates

graphs.py

1/17/2019:

• Moved prc_curve, roc_curve, and residualplots to model_evaluation.ipynb

plotly_graphs.py

1/2/2020:

• Added heatmap

model_evaluation.py

2/9/2020:

• Changed y to y_true to fix an error with specificity and negative_predictive_power

stat_functions.py

4/3/2020:

• Added cross_validated_ftest