bubble-plot 0.2

Bubble plot - data visualization package

bubble_plot

Hi everyone!

I love data visualizations! And if you love them too, I think you will find this bubble plot very nice and useful.

• How to install
• Motivation & Usage
• Usage Example
• Dependencies
• Contact

How to install

Very simple - just write in your command line:

pip install bubble_plot

Motivation & Usage

The goal for the bubble plot is to help us visualize linear and non-linear connections between numerical/categorical features in our data in an easy and simple way. The bubble plot is a kind of a 2-dimensional histogram using bubbles. It suits every combination of categorical and numerical features.

The bubble size is proportional to the frequency of the data points in this point.

Function signature:

bubble_plot(df, x, y, z_boolean=None, ordered_x_values=None, ordered_y_values=None, bins_x=10, bins_y=10, fontsize=16, 
            figsize=(15,10), maximal_bubble_size=5000, normalization_by_all = False, log=False)

For numerical features the values will be presented in buckets (ten equally spaced bins will be used as default, you can provide the specific bins / bins number through the bin_x and bins_y parameters).

For categorical features the features will be presented according to their categories. If you would like a specific order for the categories presentation please supply a list of the values by order using the ordered_x_values / ordered_y_values parameters.

You can plot a numerical feature vs. another numerical feature or vs. a categorical feature or a categorical feature vs another categorical feature or numerical feature. All options are possible.

Setting the log parameter to True would apply the natural log function - element wise - on the counts which will make the differences between the largest bubble to the smallest bubble much smaller, so if you have large differences between the frequencies of different values you might want to use that.

Setting the z_boolean parameter to a name of categorical field with two categories / boolean field would make the color of the bucket be proportional to the ratio ( (boolean_z==value_1).sum()/(boolean_z==value_1).sum() + (boolean_z==value_2).sum()) of the z values for this bucket.

Usage Example

import pandas as pd                             
from sklearn.datasets import load_boston                            
data = load_boston()                            
df = pd.DataFrame(columns=data['feature_names'], data=data['data'])                            
df['target'] = data['target']                            
bubble_plot(df, x='RM', y='target')    

The resulting bubble plot will look like this:

Dependencies

• pandas
• numpy
• matplotlib

Contact

More usage examples and explanations can be found at: https://medium.com/@DataLady/exploring-the-census-income-dataset-using-bubble-plot-cfa1b366313b

Please let me know if you have any questions. My email is [e-mail].

Enjoy, Shir